Relocation module and methods for surgical equipment

ABSTRACT

Modules for housing electronic and electromechanical medical equipment including a system to measure and record administration of one or more IV medications or fluids for IV administration. A system and method for fusing independent measures of the physiological parameters, in some examples using a Kalman filter for each possible combination of sensor measurements.

PRIORITY

This application is a continuation-in-part of U.S. application Ser. No.17/550,696, filed Dec. 14, 2021, which is a continuation of U.S.application Ser. No. 17/376,469, filed Jul. 15, 2021, now issued as U.S.Pat. No. 11,219,570, which is a continuation-in-part of U.S. applicationSer. No. 17/199,722, filed Mar. 12, 2021, now issued as U.S. Pat. No.11,173,089, which is a continuation of U.S. application Ser. No.17/092,681, filed Nov. 9, 2020, now issued as U.S. Pat. No. 10,993,865,which is a continuation of U.S. application Ser. No. 16/879,406, filedMay 20, 2020, now issued as U.S. Pat. No. 10,869,800, which is acontinuation-in-part of U.S. application Ser. No. 16/601,924, filed Oct.15, 2019, now issued as U.S. Pat. No. 10,702,436, which is acontinuation of U.S. application Ser. No. 16/593,033, filed Oct. 4,2019, now issued as U.S. Pat. No. 10,653,577, which is a continuation ofU.S. application Ser. No. 16/364,884, filed Mar. 26, 2019, now issued asU.S. Pat. No. 10,507,153, which claims the benefit of priority to U.S.Provisional Patent Application 62/782,901, filed Dec. 20, 2018. U.S.application Ser. No. 17/199,722, filed Mar. 12, 2021, is also acontinuation-in-part of U.S. application Ser. No. 15/935,524, filed Mar.26, 2018, now issued as U.S. Pat. No. 10,512,191.

U.S. application Ser. No. 17/376,469, filed Jul. 15, 2021, now issued asU.S. Pat. No. 11,219,570 is also a continuation-in-part of U.S.application Ser. No. 17,245,942, filed Apr. 30, 2021, which is acontinuation-in-part of U.S. application Ser. No. 17/167,681, filed Feb.4, 2021, now issued as U.S. Pat. No. 11,160,710, which is acontinuation-in-part of U.S. application Ser. No. 17/092,681, filed Nov.9, 2020, now issued as U.S. Pat. No. 10,993,865, which is a continuationof U.S. application Ser. No. 16/879,406, filed May 20, 2020, now issuedas U.S. Pat. No. 10,869,800.

The disclosure of each of these applications is incorporated herein byreference in its entirety.

TECHNICAL FIELD

This document pertains generally, but not by way of limitation, tosystems and methods for improving safety in operating rooms andhospitals. In particular, the systems and methods described herein mayinclude but are not limited to, anesthetic, surgical and medicalequipment storage and operational data capture, automated anesthetic andpatient monitoring data capture and electronic record input. The presentinvention relates generally to physiological measurements and, morespecifically, to a system and method for sensor fusion using multiplephysiological sensors.

BACKGROUND

Anesthesia monitors and equipment as well as surgical equipment havebeen invented, developed and sporadically introduced into surgicalpractice over more than a century. This equipment is made by a widevariety of companies who have no incentive to coordinate with oneanother to create the most efficient operating room. Equipmentthroughout the operating room has been placed in one location oranother, generally without a plan and then decades later, is stillsitting in that unplanned location.

Over the past 20 years, there has been a gradual movement to replacingpaper anesthetic records with electronic anesthetic records (EAR). Thedigital electronic data outputs of the patient's physiologic monitorshave been relatively easy to input into the EAR. However, the identity,dosing and timing of IV and inhaled drug administration, IV fluidadministration, oxygen and ventilation gas administration and anestheticevents such as intubation have required manual input to the EAR by wayof a computer keyboard and mouse. Blood, fluid and urine outputs havealso required manual input to the EAR by way of a computer keyboard andmouse. The surgical equipment scattered around the operating room eitherdoes not produce a digital output that could memorialize the equipment'soperation to the electronic record, that output is not automaticallycaptured, or the output is not provided in a way that providesmeaningful context.

Carefully observing the patient in various conditions and situationsincluding surgery has been an important source medical information forcenturies. However, in this age of electronic monitoring, patientobservation by the healthcare provider is becoming a lost art that isinfrequently done and if it is done it may not be entered into therecord, so the information is lost.

The concept of “garbage in, garbage out” is of the utmost importance formedical algorithms trained on healthcare datasets. The majority of datacategories in the acute care setting are manually inputted, basically adigitized paper record. Manually inputted data is sporadic and prone toerrors and omissions. It is also not time-stamped for temporalcorrelations. The result is that when those data are aggregated into a“big data” database, the data is incomplete, inconsistent, missing andoften unusable. Further, clinicians hate the data entry part of theirjobs, seeing it as time consuming and distracting from patient care.Several studies have linked manual data entry to the electronic recordas a significant contributor to physician burnout.

SUMMARY

In some examples, the automated data consolidation module of thisdisclosure minimizes “garbage in” of healthcare “big data” by automatingthe data input process.

As a result of the current medical practices described, the majority ofthe input to the electronic anesthetic and medical records has been thedata from the vital signs monitors, recording the patient's “response.”The “dose” events (things that are given or done to the patient leadingto the “response”) are manually entered into the record, resulting inmistakes, omissions and no temporal correlation between the dose andresponse. The incomplete and inaccurate records make any analysis withartificial intelligence and machine learning software problematic,either for that individual patient or for “big data” analysis ofpopulations of patients.

This document pertains generally to systems and methods for improvingsafety for patients receiving intravenous (IV) medications and fluids,by avoiding medication or fluid errors and documenting theadministration. This document pertains generally, but not by way oflimitation, to systems and methods for constructing granular(beat-by-beat) anesthetic, surgical and patient records that includeboth “dose” events (things that are given or done to the patient) and“response” events (inputs from electronic monitors, measurement devicesand machine vision “observations”). The dose and response events areprecisely temporally related and recorded in the patient's electronicrecord and may be pooled with the records of other patients in adatabase that can be analyzed with artificial intelligence and machinelearning software.

Illustrative examples of an automated data consolidation module thatsystematizes surgical safety for patients and OR personnel. In someexamples, this automated data consolidation module is designed to housenearly all of the operating room patient monitors and support equipment.Even dissimilar types of equipment that are normally kept separate fromone another. In some examples, this unique automated data consolidationmodule is specially designed to fit next to and under the arm-board ofthe surgical table—a location traditionally occupied by an IV pole. Forthe past 100 years, this location has been a wasted “no-man's land”between the anesthesia and surgical sides of the operating room. Inreality, the unique space next to and under the arm-board, is truly the“prime real estate” of the entire operating room: it is immediatelyadjacent the patient for optimal monitoring while simultaneouslymaintaining observation of the patient and surgical procedure; equipmentcontrols can be conveniently accessed by both the anesthesia andsurgical staff; short cables and hoses are adequate to reach thepatient; and it is uniquely accessible from both the anesthesia andsurgical sides of the anesthesia screen. The unique space next to andunder the arm-board is the only location in the entire operating roomwhere cables, cords and hoses from both the anesthesia side and thesterile surgical field side, do not need to traverse the floor or eventouch the floor in order to connect to their respective monitor orpatient support equipment—truly a remarkable location that has beenwasted by conventional systems.

In some examples, an illustrative automated data consolidation modulecan house both anesthesia related and non-anesthesia related equipment.In some examples, the illustrative relocation module can house a varietyof non-proprietary OR equipment such as patient vital sign monitors,electro-surgical generators, anesthesia machines and mechanicalventilators. In some examples, the automated data consolidation moduleis designed to also house newer proprietary safety equipment such as:air-free electric patient warming, surgical smoke evacuation, wastealcohol and oxygen evacuation, evacuation of the flow-boundarydead-zones that cause disruption of the OR ventilation and theevacuation and processing of waste heat and air discharged from ORequipment. In some examples, this automated data consolidation modulemay also house dissimilar equipment (e.g., unrelated to anesthesiamonitoring) such as: air mattress controls and air pumps; sequentialcompression legging controls and air pumps; capacitive couplingelectrosurgical grounding; RFID counting and detection of surgicalsponges; the waste blood and fluid disposal systems; and “hover”mattress inflators. Any of these devices may be stored in the automateddata consolidation module together with (or without) anesthesiaequipment.

In some examples, the automated data consolidation module is aspecialized and optimally shaped rack for holding and protecting thepatient monitors and other electronic and electromechanical surgicalequipment, in a unique location. A location that is very different fromjust setting anesthesia monitors on top of the anesthesia machine andscattering other equipment across the floor of the operating room. Theautomated data consolidation module may be used anywhere throughout thehospital or long term care settings.

The various pieces of electronic and electromechanical equipment housedwithin the automated data consolidation module disclosed herein canproduce relatively large amounts of waste heat. In some examples, theautomated data consolidation module may include a waste heat managementsystem to safely dispose of the waste heat created by the electronic andelectromechanical equipment housed within the automated dataconsolidation module.

It would be difficult or even impossible to manage the uncontained wasteheat produced by electronic and electromechanical equipment mounted on asimple open rack because it can escape in any direction. In someexamples, the module can include a “cowling” covering some or all of theouter surface. The cowling not only protects the equipment fromaccidental fluid damage but also confines the waste heat from theelectronic and electromechanical equipment mounted within the module, tothe inside of the module and cowling. In some examples, the confinedwaste heat can then be safely managed.

In some examples, the cowling cover of the automated data consolidationmodule can form or support a waste heat management system. In someexamples, the cowling can be provided on an inner surface of thehousing. In some examples, the cowling can be described as aninsulation. In some examples, the housing can include other types ofinsulation from heat and/or water. Any suitable type of insulatedhousing suitable for use in a surgical field can be provided.

In some examples, the automated data consolidation module of the instantinvention may also contain the components of the anesthesia gas machine.So-called “gas machines” are relatively simple assortments of piping,valves, flow meters, vaporizers and a ventilator. These could be locatedwithin the automated data consolidation module or attached to theautomated data consolidation module for further consolidation ofequipment and for improved access to the patient.

In some examples, locating the anesthesia machine in or on the automateddata consolidation module allows direct access for and sensors andmonitors related to the anesthesia machine, to input data to theelectronic anesthetic record being recorded by equipment in theautomated dose/response record system.

In some examples, the collection canisters for waste fluid and blood maybe conveniently mounted on the module.

In some examples, the controls and display screens for the surgicalequipment housed in the automated data consolidation module may bewirelessly connected to a portable display screen such as an iPad or“smart tablet,” for convenient access by the nurse anywhere in the room.A remote display screen can also allow remote supervision andconsultation.

In some examples the automated data consolidation module may be locatednext to the patient's bed in the ICU, ER, on the ward or in long termcare. While most of the data collected by the automated dataconsolidation module will occur in the acute care setting, it should beunderstood that the automated data consolidation module concept forautomatically collecting and consolidating data from a wide variety ofdata sources including monitors and other medical equipment, can beapplied throughout the healthcare delivery system.

Doctors and nurses dislike record keeping and the switch to theelectronic record has made the act of record keeping more difficult andtime consuming. Entering the electronic record into the computersometime after the event occurred and the case has settled down, is notonly distracting from patient care but leads to inaccurate records. Handentered records also bypass the opportunity for the computer to add topatient safety by checking drug identities, dosages, side effects,allergies and alerting the clinician to potential problems or evenphysically stopping the drug administration. Manually entered recordsare not useful for managing drug inventories because a given medicationadministration is not tied to a specific drug bottle or syringe.Finally, the computer mouse and keyboards have been shown to becontaminated by a wide variety of infective organisms and are virtuallyimpossible to clean. Automatic anesthetic data entry to the EAR wouldimprove patient safety, improve clinician job satisfaction and improveOR inventory management.

In general, doctors and nurses are not interested in replacingthemselves and their jobs with automated drug delivery or automatedanesthesia systems. However, they may be more open to automated recordkeeping. The challenge with automated record keeping is automating thedata input that documents the numerous activities, anesthesia relatedevents, fluid, gas and medication administration that constitute ananesthetic experience or another medical situation.

The second challenge in implementing an automated electronic anestheticrecord (EAR) or automated electronic medical record (EMR) is to force aslittle change in routine as possible onto the anesthesiologist and otherclinicians using this system. Anesthesiologists and surgeons arenotoriously tradition-bound and resistant to any changes in their “triedand true” way of doing things. Therefore, a successful automated EARmust interact seamlessly with current anesthesia practices and operatingroom workflow without causing any disruptions.

In some examples, the automated data consolidation module of thisdisclosure includes a system for automatically measuring and recordingthe administration of IV medications and fluids. The system forautomatically measuring and recording the administration of IVmedications and fluids can include one or more sensors, such as one ormore of a barcode reader and an RFID interrogator for accuratelyidentifying a medication or fluid for IV administration.

In some examples, the system for automatically measuring and recordingthe administration of IV medications and fluids can also include one ormore digital cameras with machine vision software (“machine vision”) foraccurately measuring the volume of medication administered from asyringe or fluid administered from an IV bag through a drip chamber intoan IV tubing. The digital cameras with machine vision softwareessentially duplicate the clinician's vision of an activity, injectionof a drug from a syringe for example, without interfering in the normalactivity and yet allows automatic recording of the activity in the EAR.The machine vision software can include one or more machine-readablemediums that when implemented on hardware processing circuitry of thesystem or in electrical communication with the system, can perform thefunctions described herein.

In some examples, the automated data consolidation module of thisdisclosure uses machine vision to unobtrusively “observe” the flow rateof the ventilation gas flow meters and inhaled anesthetic vaporizers.

In some examples, the automated data consolidation module of thisdisclosure captures input data from the blood and fluid collection andurine output collection systems of this disclosure.

In some examples, the automated data consolidation module of thisdisclosure lets the computer (e.g., a processor and memory forperforming instructions) add to patient safety by checking drugidentities, dosages, side effects, allergies, the patients' medicalhistory and vital signs and alerting the clinician to potential problemsor even physically stopping the drug administration. In some examples,the automated data consolidation module of this disclosure eliminatesmedication errors by checking the drug to be injected against thephysician's medication orders before the injection can occur. In someexamples, the automated data consolidation module of this disclosure isuseful for managing drug inventories because a given medicationadministration is tied to a specific drug bottle or syringe.

In some examples, the automated data consolidation module of thisdisclosure may also automatically record and display many otherfunctions including but not limited to: IV fluid administration,medication infusions, the patient's vital signs, urine output, bloodloss, ventilator settings, inspired gases, electrosurgical settings,pneumoperitoneum insufflation settings, RFID surgical sponge counts,surgical information and video, dialysis or other medical procedureinformation and patient activity.

“Dose/response” is one of the most basic of all medical processes. Sincethe beginning of medical practice, both the art and science of medicinehave relied on giving something to the patient (a medicine for example)or doing something to the patient (mechanical ventilation or surgery forexample)—the “dose”, and then observing the patient's “response.” Theproblem now seen with electronic records is that the only data that istimely recorded is the “response” data provided by the physiologicmonitors. Even that response data is frequently not recordedbeat-by-beat but rather intermittently recorded every 5 minutes or 30minutes or 4 hours for example. All of the “dose” data is entered intothe electronic record by hand and therefore is prone to mistakes,omissions and unknown timing. Therefore, with current EMRs, the dose andresponse data cannot be temporally correlated with any accuracy, vastlyreducing the analytical and predictive value of the electronic databaseand record.

In some examples, the automated data consolidation module of thisdisclosure includes systems and methods for constructing granular(beat-by-beat, second-by-second) anesthetic, surgical and patientrecords that include both “dose” events—the things that are given ordone to the patient (inputs from medication injection and fluidmonitors, various support equipment and machine vision “observations”for example) and “response” events (inputs from electronic monitors,measurement devices and machine vision “observations” for example). Insome examples, the invention of this disclosure automatically entersboth dose and response events into the electronic record. In someexamples, the invention of this disclosure automatically enters bothdose and response events into the electronic record and temporallycorrelates the dose and response events, such as but not limited to,when they are recorded. In some examples, the automatically entered,temporally correlated dose and response events in the patient'selectronic record may be analyzed by artificial intelligence (AI) and/ormachine learning (ML) software stored in a memory of a storage deviceelectrically coupled to the processing circuitry of the module forimmediate advice, alerts and feedback to the clinician. In someexamples, the automatically entered, temporally correlated dose andresponse events in the patient's electronic record may be pooled withthe records of other patients in a database that can be analyzed withartificial intelligence and machine learning software.

Machine Learning (ML) is an application that provides computer systemsthe ability to perform tasks, without explicitly being programmed, bymaking inferences based on patterns found in the analysis of data.Machine learning explores the study and construction of algorithms(e.g., tools), that may learn from existing data and make predictionsabout new data. Such machine-learning algorithms operate by building anML model from example training data, in order to make data-drivenpredictions or decisions expressed as outputs or assessments. Theprinciples presented herein may be applied using any suitablemachine-learning tools.

In some examples, the AI or ML software can compare dose and responseevents from different periods of time for the same patient to learn andidentify the particular patient's responses. In some examples themachine learning software can be trained to identify a patient'sresponses using training obtained from a plurality of patients that mayinclude or not include the patient being monitored. Any suitable AI orML can be implemented to interpret the data generated by the module.Current methods of obtaining data in medical settings cannot generate,store or aggregate such data for analysis using AI or ML. Thus, thedose-response systems described herein provide a technical solution to atechnical problem.

Machine vision cameras and software are very good at measuringdistances, movements, sizes, looking for defects, fluid levels, precisecolors and many other quality measurements of manufactured products.Machine vision cameras and software can also be “taught” through AI andML to analyze complex and rapidly evolving scenes, such as those infront of a car driving down the road.

In some examples, the automated data consolidation module of thisdisclosure includes novel systems and methods for using machine visioncameras and software to “observe” the patient. If the patient is insurgery, the patient's head may be the focus of the observation. In someexamples, during surgery the machine vision cameras and software may be“looking” for dose events including but not limited to mask ventilationor endotracheal intubation. In some examples, during surgery the machinevision cameras and software may be “looking” for response eventsincluding but not limited to grimacing or tearing or coughing or changesin skin color.

If the patient is on the ward or in the nursing home or other long-termcare facility, the whole patient may be the focus of the observation. Insome examples, if the patient is on the ward or in the nursing home orother long-term care facility the machine vision cameras, processingcircuitry and software may be configured to “look” for dose events(e.g., sense) including but not limited to repositioning the patient,suctioning the airway or assisting the patient out of bed or any othernursing procedure, eating and drinking. In some examples, if the patientis on the ward or in the nursing home or other long-term care facility(including at home) the machine vision cameras, processing circuitry andsoftware may be configured to “look” for response events (e.g., sense)including but not limited to restlessness or getting out of bed withoutassistance or coughing or breathing pattern. In some examples, thesystem can go beyond traditional physiologic monitors. Even physiologicresponse information such as pain may be detected by facial expressionanalysis.

In some examples, vital signs such as heart rate, respiration rate,blood oxygen saturation and temperature can be measured (e.g., sensed,monitored) remotely via camera-based methods. Vital signs can beextracted from the optical detection of blood-induced skin colorvariations—remote photoplethysmography (rPPG).

In some examples, the automated data consolidation module may allowremote viewing of the displayed patient information. In some examples,the remotely displayed patient information may be used for remotemedical supervision such as an anesthesiologist providing remotesupervision to a nurse anesthetist who is administering the anesthetic.In some examples, the remotely displayed patient information may be usedfor remote medical consultation. In some examples, the remotelydisplayed patient information may be used to document the involvement ofremote medical supervision or consultation for billing purposes.

In some examples, the automated data consolidation module allows rulesto be applied to the various medical equipment that is housed within themodule, mounted on the module, or is in electrical communication with orin wireless communication with the module. In some examples, the rulescan include one or more of the following: that all equipment producedata reflecting the equipment's operating parameters and sensor inputs,the data is produced in prescribed data formats, the data include allprescribed input record fields for that specific type of equipment, thedata is instantly and continuously provided.

In some examples, the automated data consolidation module includesprocessing circuitry and software that accept the data inputted from thevarious medical equipment. In some examples, the processing circuitryand software can translate data that is not inputted in the prescribedformat. In some examples, the processing circuitry and software can addtime stamps to the data to add a temporal context. In some examples, theprocessing circuitry and software can do data “filtering” in thepresence of large size data to discard information that is not usefulfor healthcare monitoring based on a defined criterion. This may includefor example, intermittently recording data that changes slowly such asthe patient's temperature, rather than continuously recording. In someexamples, the processing circuitry and software can do data “cleaning”such as normalization, noise reduction and missing data management.Sensor fusion is a technique that may be utilized to simultaneouslyanalyze data from multiple sensors, in order to detect erroneous datafrom a single sensor. In some examples, the processing circuitry andsoftware can be used in many other ways to cleanse, organize and preparethe input data.

In some examples, the processing circuitry and software execute “streamprocessing” for applications requiring real-time feedback. In someexamples, streaming data analytics in healthcare can be defined as asystematic use of continuous waveform and related medical recordinformation developed through applied analytical disciplines, to drivedecision making for the patient care.

In some examples, when the objective is to deliver data to a “big data”database, the data must be pooled. Data in the “raw” state needs to beprocessed or transformed. In a service-oriented architectural approach,the data may stay raw and services are used to call, retrieve andprocess the data. In the data warehousing approach, data from varioussources is aggregated and made ready for processing, although the datais not available in real-time. The steps of extract, transform, and load(ETL) can be used to cleans and ready data from diverse sources.

In some examples, with “big data” database data, the processingcircuitry and software may execute “batch processing,” analyzing andprocessing the data over a specified period of time. Batch processingaims to process a high volume of data by collecting and storing batchesto be analyzed in order to generate results. In some examples, theprocessing circuitry and software can serve as a “node” in batchcomputing, where big data is split into small pieces that aredistributed to multiple nodes in order to obtain intermediate results.Once data processing by nodes is terminated, outcomes will be aggregatedin order to generate the final results.

In some examples, the present invention is embodied in a system andmethod for the fusion of physiological sensor measurements from asubject. A plurality of sensors are coupled to the subject, with each ofthe sensors capable of producing a signal related to a physiologicalparameter. A statistical model affecting the fusion of the sensormeasurements is used by a statistical filter circuit.

In some examples, the statistical filter circuit receives thestatistical model and the physiological signals and produces a parameterestimate for each possible combination of the sensor measurementswherein each of the sensor measurements can be considered to beacceptable or unacceptable in producing the parameter estimates for eachpossible combination of the sensor measurements. In some examples, aconfidence calculator coupled to the filter circuit receives theparameter estimates and determines a confidence level value for each ofthe parameter estimates. The confidence level is indicative of theaccuracy of each of the parameter estimates and the confidencecalculator selects the parameter estimate based on the confidence level.

In some examples, the statistical filter can also receive a previousestimate of the physiological parameter selected by the confidencecalculator. In this embodiment the statistical filter produces theparameter estimates based on the measurement from the physiologicalsignals, the statistical model, and the past estimate. In oneembodiment, the statistical model is a parameter variability statisticalmodel that characterizes changes in the parameter over time.

In some examples, the statistical model may also be an error modelcharacterizing the susceptibility of the sensor measurements to nominalerror. Alternatively, the system may include two models, with the firstmodel characterizing the sensor error and the additional modelcharacterizing the parameter variability. The statistical filter usesthe model or models to determine the parameter estimates for each of thepossible sensor measurement combinations. In one embodiment, the systemassumes a Gaussian probability density function for the statistic model.

The system can also be adaptive in that the statistical model can beupdated following the selection of a parameter estimate by theconfidence calculator. The statistical filter circuit may be a Kalmanfilter for each of the possible sensor combinations wherein the Kalmanfilters use the previous parameter estimate and the statistical model toproduce the parameter estimates.

In some examples, the system is also susceptible to artifactinterference that causes a particular sensor measurement to beconsidered unacceptable. The confidence calculator analyzes theparameter estimates generated by the statistical filter circuit anddetermines a statistical probability of error of each of the parameterestimates caused by the artifactual measurement. Thus, the system cangenerate a fused estimate for sensors contaminated by a nominal error,as characterized by the statistical model, as well as sensorcombinations in which a sensor is affected by artifact. In someexamples, the system may also combine the sensor error statistical modeland the statistical probability of contamination by artifact todetermine a parameter estimate.

In some examples, in one sensor combination, all sensor measurements arecontaminated by artifact and considered to be unacceptable. The systemcalculates a minimum confidence level for this sensor combination andfurther selects the parameter estimate the highest probability ofcontamination by artifact while simultaneously having a minimumprobability of the confidence level exceeding the calculated minimumconfidence level.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various examples discussed in the presentdocument. Any combination of the features shown and described in thisdisclosure, including combinations of fewer or more features is withinthe content of this disclosure. Modules, systems and methods includingindividual features described herein, without combinations of featuresas shown in the examples (for the sake of brevity), are also within thescope of this disclosure.

FIG. 1 illustrates an isometric view of an example module including asystem for generating an automated electronic anesthetic record locatedproximate to a patient, in accordance with at least one example of thisdisclosure.

FIG. 2 illustrates an isometric view of an example module including asystem for generating an automated electronic anesthetic record locatedproximate to a patient, in accordance with at least one example of thisdisclosure.

FIG. 3 illustrates a plan view of an example of preloaded syringes thatcan be used with the system of FIGS. 1 and 2, in accordance with atleast one example of this disclosure.

FIG. 4 illustrates a side view of an example medication identificationand measurement system and a syringe that can be used with the system ofFIGS. 1 and 2, to monitor drug delivery, in accordance with at least oneexample of this disclosure.

FIG. 5 illustrates a cross-sectional view of the medicationidentification and measurement system and a syringe (not shown incross-section) of FIG. 4, taken along line 5-5, in accordance with atleast one example of this disclosure.

FIG. 6 illustrates a side view of a second example medicationidentification and measurement system and a syringe that can be usedwith the system of FIGS. 1 and 2, in accordance with at least oneexample of this disclosure.

FIG. 7 illustrates a cross-sectional view of the second example of amedication identification and measurement system and the syringe (notshown in cross-section) of FIG. 6, taken along line 7-7, in accordancewith at least one example of this disclosure.

FIG. 8 illustrates a side view of a third example of a medicationidentification and measurement system and a syringe that can be usedwith the system of FIGS. 1 and 2, in accordance with at least oneexample of this disclosure.

FIG. 9 illustrates a cross-sectional view of the third example of amedication identification and measurement system and the syringe (notshown in cross-section) of FIG. 8, taken along line 9-9, in accordancewith at least one example of this disclosure.

FIG. 10 illustrates an example injection port cassette that can be usedwith the system of FIGS. 1 and 2, as detailed in FIGS. 5, 7 and 9, inaccordance with at least one example of this disclosure.

FIG. 11 illustrates a plan view of an example of healthcare provider IDbadges that can be used with the system of FIGS. 1 and 2, in accordancewith at least one example of this disclosure.

FIG. 12 illustrates an isometric view of another example moduleincluding a system for generating an automated electronic anestheticrecord located proximate to a patient, in accordance with at least oneexample of this disclosure.

FIG. 13 illustrates an isometric view of another example moduleincluding a system for generating an automated electronic anestheticrecord located proximate to a patient, in accordance with at least oneexample of this disclosure.

FIG. 14 illustrates a side view of an example IV fluid identificationand measurement system that can be used with the systems of FIGS. 1 and2, and injection port cassette of FIG. 10, in accordance with at leastone example of this disclosure.

FIG. 15 illustrates generally an example of a block diagram of a machine(e.g., of module 101, 201) upon which any one or more of the techniques(e.g., methodologies) discussed herein may perform in accordance with atleast one example of this disclosure.

FIG. 16 is a flow chart illustrating a technique of IV fluididentification and measurement, in accordance with at least one exampleof this disclosure.

FIG. 17 is a second flow chart illustrating the technique of IV fluididentification and measurement, in accordance with at least one exampleof this disclosure.

FIG. 18 is a flow chart illustrating a technique of medicationidentification and measurement, in accordance with at least one exampleof this disclosure.

FIG. 19 is a second flow chart illustrating a technique of medicationidentification and measurement, in accordance with at least one exampleof this disclosure.

FIG. 20 is a flow chart illustrating a second technique of IV fluididentification and measurement including safety and security aspects, inaccordance with at least one example of this disclosure.

FIG. 21 is a second flow chart illustrating a second technique of IVfluid identification and measurement including safety and securityaspects, in accordance with at least one example of this disclosure.

FIG. 22 illustrates generally an example of a block diagram of vendingsystem and a medication delivery system of FIGS. 1-21 upon which any oneor more of the techniques (e.g., methodologies) discussed herein mayperform in accordance with at least one example of this disclosure.

FIG. 23 is a flow chart illustrating a technique 2300 for assessingphysiologic events, in accordance with at least one example of thisdisclosure.

FIG. 24 is a flow chart illustrating a technique 2400 for assessingphysiologic events, in accordance with at least one example of thisdisclosure.

FIG. 25 is a flow chart illustrating a technique 2500 for assessingphysiologic events, in accordance with at least one example of thisdisclosure.

FIG. 26 is a flow chart illustrating a technique 2600 for creating bigdata, in accordance with at least one example of this disclosure.

FIG. 27 is a functional block diagram of the system of the presentinvention, in accordance with at least one example of this disclosure.

FIG. 28 illustrates possible types of error contaminating theobservations of the system of FIG. 27, in accordance with at least oneexample of this disclosure.

FIG. 29 is a more detailed functional block diagram of the system ofFIG. 27, in accordance with at least one example of this disclosure.

FIG. 30 is a flow chart used by the system of FIG. 29 to analyzephysiological data, in accordance with at least one example of thisdisclosure.

DETAILED DESCRIPTION

The following detailed description is exemplary in nature and is notintended to limit the scope, applicability, or configuration of theinvention in any way. Rather, the following description providespractical illustrations for implementing exemplary examples of thepresent invention. Examples of constructions, materials, dimensions, andmanufacturing processes are provided for selected elements, and allother elements employ that which is known to those of skill in the fieldof the invention. Those skilled in the art will recognize that many ofthe examples provided have suitable alternatives that can be utilized.

Physiological measurements provide a physician or other medicalprofessional with a quantitative indication of the patient's condition.A variety of different sensors provide physiological measurements ofmany different physiological events. Some of the sensors may provide ameasurement of the same physiological event. For example, heart rate canbe determined from an electrocardiograph (ECG) machine but may also bederived from other sensors.

It is known that a patient's present heart rate can only be measuredfrom various physiological signals. For example, the ECG provides ameasure of heart rate over a predetermined period of time. The heartrate estimate is derived by measuring the interval between heartbeatsfor the predetermined period of time, and performing mathematicalcalculations based on the interval measurements to derive the heart rateestimate. However, the ECG signal may have been contaminated by noiseduring the predetermined period resulting in an error in the heart ratemeasurement. Thus, no measurement system can provide a heart ratemeasurement with complete assurance of accuracy. Other sensors attachedto the human body can also estimate heart rate based on a variety ofphysiologically-based signals. For example, a blood pressure monitor, orpulse oximeter can be used to derive a heart rate estimate.

Each of these physiologically-based signals is subject to interferencesuch as patient movement, patient breathing, or electrical interference.If the level of the interference is sufficiently low with respect to thesignal, the signal may still provide accurate data although there may besome acceptable level of error. If the interference is at a relativelyhigh level, the signal may no longer provide accurate data. Thesehigh-level interference sources tend to result in a measurement with anunacceptably high level of error and is therefore called “artifact.”When excessive interference is present at a particular sensor, thatsensor will suddenly provide an incorrect physiological measurement(i.e., artifact) from the perspective of the observer, who is usually aphysician or other skilled expert. In order to determine which of themultiple sensors are providing acceptable or consistent observations,the observer relies on his experience and knowledge about: (1) thenature of physiological parameter being measured and its variabilityfrom a physiological perspective; (2) the susceptibility of differentsensors to various types of error, and particularly to artifact; and (3)consensus among the various sensor measurements. Thus, the observer mustanalyze the data from each sensor and manually determine the correctvalue based on his judgment as to the most reliable measurement. Theobserver also uses his own knowledge of the parameter's characteristicsas well as experience gained in monitoring the specific patient todetermine the correct value. Such manual analysis and decision makingrequires a significant amount of time on the part of the observer.

Therefore, it can be appreciated that there is a significant need for asystem and method for automatically analyzing physiological data fromvarious sensors to determine a reliable measure of the physiologicalevent without the interpretation of different measurements by theobserver. The present invention provides this and other advantages aswill be apparent from the following figures and accompanying detaileddescription.

The disclosure of Feldman et al., U.S. Pat. No. 5,626,140, issued May 7,1997, is hereby incorporated by reference in its entirety. Anyincorporation by reference of documents herein is limited such that nosubject matter is incorporated that is contrary to the explicitdisclosure herein. Any incorporation by reference of documents herein isfurther limited such that no claims included in the documents areincorporated by reference herein.

As described herein, operably coupled can include, but is not limitedto, any suitable coupling, such as a fluid (e.g., liquid, gas) coupling,an electrical coupling or a mechanical coupling that enables elementsdescribed herein to be coupled to each other and/or to operate togetherwith one another (e.g., function together).

“Dose/response” can be a useful medical tool. Dose/response involvesgiving something to the patient (a medicine for example) or doingsomething to the patient (mechanical ventilation or surgery forexample)—the “dose”, and then observing the patient's “response.”

Throughout this disclosure, the word “dose” or “dose event” meanssomething that was given to or done to the patient. In some examples,“dose events” involve giving something to the patient including but notlimited to: the injection or ingestion of medications, the infusion ofmedications, the infusion of IV fluids, inspired gases such as oxygen ornitrous oxide or volatile anesthetics or nitric oxide.

In some examples, “dose events” are things done to the patient,including but not limited to: mask ventilation, tracheal intubation,mechanical ventilation, pneumoperitoneum insufflation, patientpositioning and surgery.

In some examples, “dose events” are applications of electricity to thepatient, including but not limited to: train-of-four determination ofmuscle relaxation, nerve conduction studies, cardiac pacing,cardioversion, electrical stimulation of nerves and electroconvulsivetherapy. Many other “dose events” are anticipated.

Throughout this disclosure, the word “response” or “response event”means a physiologic, reflexive motor response or volitional motorresponse from the patient. In some examples, “response events” involvemeasuring a physiologic response of the patient measured by thephysiologic monitors, including but not limited to: an electrocardiogram(EKG), pulse oximetry, blood pressure, end tidal gases,electroencephalogram (EEG), bispectral index (BIS), laboratory bloodstudies, urine output, blood loss and pulmonary compliance.

In some examples, “response events” involve measuring a motor responseof the patient, including but not limited to: a train-of-four,grimacing, coughing, movements of many sorts and tearing, that may bemeasured by a machine vision camera and software.

FIG. 1 illustrates an isometric view of an example automated dataconsolidation module100 for generating an automated electronicanesthetic record (EAR) or electronic medical record (EMR) locatedproximate to a patient. Some aspects of FIG. 1 are also described withrespect to the description of other figures, including FIG. 14.

As shown in FIG. 1, the automated data consolidation module 100 may beattached to and portions can be stored within a module 101. The module101 can conveniently provide direct access to the patient 102. An IVpole 105 may provide a convenient mounting support location for theautomated data consolidation module for IV medications 100 (hereinafter,“automated data consolidation module 100”). In some examples, thecomponents and systems of the automated data consolidation module100 ofthis disclosure can be supported by other mounting supports, includingbut not limited to a boom-mounted rack system, a wheeled rack system anda bed 103 mounting bracket. One or more computers including processingcircuitry 157, of the automated data consolidation modu1e100 of thisdisclosure may be conveniently and safely housed inside the module 101.

In some examples, it is anticipated that some or all of the componentsof the automated data consolidation module100 of this disclosure couldbe used in other healthcare settings such as the intensive care unit,the emergency room or on the ward. As shown in FIG. 1, the module 101may be mounted on an IV pole 105 or other suitable mounting structurelocated near the patient 102.

In some examples, a touch-screen electronic record display 126 canconvert to a qwerty-type keyboard to allow uncommon anesthetic andsurgical events or deviations from pre-recorded scripts, to be manuallydocumented. In some examples, natural language speech recognitionsoftware may be included in the processing circuitry 157 allowing theoperator to simply dictate the record. This allows the standard computerkeyboard that is used for data entry in most electronic anestheticrecords, to be eliminated. Standard keyboards are known to becontaminated with pathogenic organisms and are nearly impossible toclean and decontaminate due to their irregular surfaces. In contrast,the smooth glass or plastic face of a touch-screen monitor is easy toclean with no crevasses to hide organisms.

In some examples, the automated data consolidation module for IVmedications 100 of this disclosure can include a system forautomatically measuring and recording the administration of IVmedications. In some examples, the system for automatically measuringand recording the administration of IV medications includes a medicationidentification and measurement system 128. In some examples, aspects ofthe automated data consolidation module100 can be provided together withor separately from other aspects of the IV medication identification andmeasurement system 128 (hereinafter, “medication identification andmeasurement system 128”). Likewise, aspects of the medicationidentification and measurement system 128 can be provided together withor separately from other aspects of the automated data consolidationmodule 100.

FIG. 2 illustrates an isometric view of an example automated dataconsolidation module 200 for generating an automated electronicanesthetic record located proximate to a patient 202. Features of theautomated data consolidation module 100 of FIG. 1 may be included in theautomated data consolidation module 200 of FIG. 2, and vice-versa,therefore all aspects may not be described in further detail. Likenumerals can represent like elements. Aspects of FIGS. 1 and 2 may alsobe described together. Some aspects of FIG. 2, including an IV fluididentification and measurement system 130, are described with respectother figures, including the description of IV fluid identification andmeasurement system 1430 of FIG. 14.

As shown in FIG. 2, an example medication identification and measurementsystem 228 may be attached to a relocation module 201 that may beadvantageously positioned proximate the patient 202, such as near thepatient's head on a surgical table 212. In this position medications canbe conveniently administered by medical personnel while also tending toand observing the patient 202 during surgery.

It would be difficult or even impossible to manage the uncontained wasteheat produced by electronic and electromechanical equipment mounted on asimple open rack because it can escape in any direction. In someexamples, the automated data consolidation module 100,200 can include acowling (e.g., 299C; FIG. 2) covering substantially the entire outersurface of the housing 299. The cowling 299C not only protects theequipment from accidental fluid damage but also confines the waste heatfrom the electronic and electromechanical equipment mounted within theautomated data consolidation module 100,200 to the inside of the module201 and cowling 299C. In some examples, the confined waste heat can thenbe safely managed.

In some examples, the cowling 299C cover of the automated dataconsolidation module 100,200 can form or support a waste heat managementsystem. In some examples, the cowling 299C can be provided on an innersurface of the housing 299. In some examples, the cowling 299C can bedescribed as an insulation. In some examples, the housing 299 caninclude other types of insulation from heat and/or water. Any suitabletype of insulated housing suitable for use in a surgical field can beprovided.

In some examples, the medication identification and measurement system128 (FIG. 1), 228 (FIG. 2) may include one or more sensors, such as oneor more of: a barcode reader or QR code reader (e.g., 436, FIG. 4), aradio-frequency identification (RFID) interrogator (e.g., 438, FIG. 4),or any other suitable sensor for accurately and reliably identifying amedication for IV administration. As defined herein, a barcode readercan include any other type of identifying reader, such as, but notlimited to, a QR code reader. Likewise, the RFID interrogator can be anytype of interrogator and is not limited to those interrogators based onradio frequency. Examples of such sensors are described herein, such asin FIGS. 4 and 5.

In some examples, instead of, or in addition to one or more of an RFIDinterrogator 438 and a barcode reader 436, the medication identificationand measurement system 128, 228 can receive an input to determine theidentity. For example, the medication identification and measurementsystem 128, 228 can include one or more of: a sensor, such as barcodereader 436 of FIG. 4, configured to identify the one or more IVmedications or fluids, or an input configured to receive the identity ofthe one or more IV medications or fluids, such as via the anestheticrecord input component 224.

In some examples, the barcode reader (e.g., 436, FIG. 4) may be a“computer vision” or “machine vision” camera with the capability ofreading barcodes. The term “machine vision” is often associated withindustrial applications of a computer's ability to see, while the term“computer vision” is often used to describe any type of technology inwhich a computer is tasked with digitizing an image, processing the datait contains and taking some kind of action. In this disclosure the terms“machine vision” and “computer vision” may be used interchangeably.Traditionally, machine vision includes technology and methods used toprovide imaging-based automatic inspection and analysis, processcontrol, and robot guidance. Machine vision is sometimes used inmanufacturing environments. Machine vision refers to many technologies,software and hardware products including processing circuitry,integrated systems and methods.

The inventors have discovered that machine vision can be useful beyondits traditional uses. The inventors discovered that machine vision canbe advantageous in implementing an automated data consolidation module100, 200 because it offers reliable measurements, gauging, objectrecognition, pattern recognition and liquid fill level measurements.Machine vision does not get tired or distracted. Machine vision excelsat quantitative measurement of a structured scene because of its speed,accuracy and repeatability. However, it may require the scene to bestructured to perform the desired function.

Machine vision can be very accurate for measuring size of an object at aknown distance or the distance of an object of known size. However, itcannot do both. Therefore, in some examples it is important to know theexact location of a syringe (e.g., 406, FIG. 4) and thus know thedistance from the camera (e.g., 436, FIG. 4) to the syringe (e.g., 406,FIG. 4) in order for the machine vision to calculate the distance of themovement of the plunger (e.g., 446, FIG. 5) within the syringe (e.g.,406, FIG. 5). This is what we mean by the “scene being structured.”

Machine vision may be advantageous for the automated data consolidationmodule 100, 200 of this disclosure because it “sees” and measures butdoes not touch or interfere with the healthcare provider doing theirnormal job of injecting medications or administering IV fluids. Further,the same visual image that is used by the machine vision software can betransmitted and displayed on a screen 126, 226 to give the operator(whose fingers can be pushing the plunger 446 of the syringe 406, aclose-up view of the syringe 406. FIG. 5 is a cross-section view takenat 5-5 of FIG. 4. The machine vision camera 436 can be looking at thesame view of the syringe 406 as the operator and it is the same orsimilar view that the operator would see if they were injecting IVmedications the traditional way.

The machine vision camera, or digital camera, can include machine visionsoftware, or the machine vision camera can be in electricalcommunication with (e.g., operably coupled to) one or more hardwareprocessors, such as processing circuitry 157, 257 and one or moremachine-readable mediums 159, 259. The one or more machine-readablemediums 159, 259 can include instructions (e.g., software), that whenimplemented on the processing circuitry 157, 257, can perform thefunctions described herein. The processing circuitry 157, 257 can bestored in the module 101, relocation module 201 or remote from themodules 101, 201 (e.g., in a wired or wireless manner). The one or moremachine-readable mediums 159 can be a storage device, such as a memorylocated in the module 201 or remote from the module 101, 201.

In some examples, the RFID interrogator 438 may be either High Frequency(HF) or Near Field (NF) RFID in order to advantageously limit theread-range to a distance of less than 12 inches. In some examples, theRFID read-range may advantageously be limited, such as to less than 8inches so that only a specific medication injection is identified at anytime. In a possibly more preferred example, the RFID read-range may belimited to less than 4 inches to further prevent mis-readings. NF-RFIDhas a short read-range by definition and the read-range of HF-RFID canbe easily limited by restricting the size of the antenna on the tag. Incontrast, longer read-range RFID such as Ultra-high Frequency (UHF-RFID)may confusingly interrogate every RFID tag in the operating room andthus be unable to identify which medication is being delivered to themedication identification and measurement system 128, 228. However, anysuitable RFID range for a particular application may be used.

FIG. 3 illustrates a plan view of an example of preloaded syringes 306Aand 306B that can be used with the automated data consolidation module200 of FIG. 2.

The one or more preloaded syringes 306A and 306B may be labeled with aunique barcode label 307 or an RFID tag 308 that may identify one ormore of the drug, the concentration, the lot number, the expirationdate, the manufacturer and other important information. In someexamples, a unique barcode label 307 may be a “2-D” barcode label inorder to include more information on a smaller area than traditionalbarcode labels. In some examples, the barcode label 307 or RFID tag 308includes the drug identifying label 309A and 309B for convenient use bythe caregiver.

In some examples, the syringes 306A and 306B can be filled at the pointof use and may be labeled with drug labels 309A and 309B and eitherbarcode labels 307 or RFID tags 308 that are removably attached to thedrug bottle or vial at the factory or pharmacy. The drug labels 309A and309B and either barcode labels 307 or RFID tags 308 may be easilyremoved from the drug bottle or vial and adhesively attached to thesyringe 306A or 306B at the time that the syringe 306A or 306B is loadedwith the drug by the caregiver. Instead of, or in addition to thebarcode labels 307 or RFID tags 308, any other suitable “tag/reader”system known in the arts, may be used.

FIGS. 4-10 illustrate examples of medication identification andmeasurement systems 428, 628, 828 that can be used with the automateddata consolidation module 100, 200 of FIGS. 1 and 2. However, aspects ofthe medication identification and measurement systems 428, 628 and 828may be used with other systems, and other medication identification andmeasurement systems may be used with the automated data consolidationmodule 100, 200. Furthermore, some examples of the automated dataconsolidation module 100, 200 can omit aspects of the medicationidentification and measurement systems, or can omit a medicationidentification and measurement system altogether. FIG. 4 illustrates aportion of an automated data consolidation module 400 including a sideview of an example medication identification and measurement system 428and a syringe 406 that can be used with the automated data consolidationmodule 100, 200 of FIGS. 1 and 2, to monitor drug delivery. FIG. 5illustrates a cross-sectional view of the medication identification andmeasurement system 428 and the syringe 406 (not shown in cross-section)of FIG. 4, taken along line 5-5. FIGS. 4 and 5 are described together.

As shown in FIGS. 4 and 5, the medication identification and measurementsystem 428 may include at least one injection portal 411. The injectionportal 411 may be a receptacle for accommodating a syringe 406 in afixed and known location and can be configured to orient the Luer taperconnector 513 to mate with an injection port 515. The injection port 515can be secured within the injection portal 411 and can be in fluidcommunication with IV tubing 520. In some examples, the injection portal411 may include an injection portal tube 416, such as a transparent tubethat is sized to receive and accommodate a syringe barrel 418 of asyringe 406. In some examples, the injection portal can be configured toreceive a specific size syringe barrel 418. In some examples, multipleinjection portals 411 can be provided to accommodate syringes 406 ofdifferent sizes.

FIG. 6 illustrates a portion of an automated data consolidation module600 including a side view of a second example of a medicationidentification and measurement system 628 and a syringe 606 that can beused with the automated data consolidation module 100, 200 of FIGS. 1and 2, to monitor drug delivery. FIG. 7 illustrates a cross-sectionalview of the second example of a medication identification andmeasurement system 628 and the syringe 606 (not shown in cross-section)of FIG. 6, taken along line 7-7. FIGS. 6 and 7 are described together.

As shown in FIGS. 6 and 7, the injection portal 611 of the medicationidentification and measurement system 628 may be large enough toaccommodate syringes 606 of multiple sizes within the space defined by areal or imaginary injection portal tube 616. In this example, accuratelyorienting the Luer taper connector 713 to mate with an injection port715 may be accomplished by one or more orienting members such as one ormore spring positioning members 622A-F that engage with the syringebarrel 618 to center it in the injection portal 611. In some examples,there may be two or more rows of spring positioning members 622A-F. Forexample, spring positioning members 622A, B, E, F may be located nearthe entrance to the injection portal 611 and spring positioning members622C, D may be located near the injection port 715 to assure accuratepositioning for mating with the Luer taper connector 713. Springpositioning members 622A-F may include not only spring wires or metal orpolymer or plastic spring pieces but any flexible material orcombination of materials or shapes that can be deformed by the syringebarrel 618 entering the injection portal 611 and retain a memory (e.g.,elastically deformable, substantially elastically deformable,resiliently deformable, resilient member) so as to urge the syringebarrel 618 into a centered position within the space defined by a realor imaginary injection portal tube 616.

One objective of the spring positioning members 622A-F can be to“automatically” center and align the Luer taper connector 713 of thesyringe 606 with the injection port 715, so that the operator can simplyand conveniently push the syringe 606 into the injection portal 611 andno further manual alignment may be needed. The spring positioningmembers 622A-F can also obviate the need for the operator to toucheither the Luer taper connector 713 of the syringe 606 or the injectionport 715, thus beneficially preventing accidental infectiouscontamination by the operators' fingers and gloves.

FIG. 8 illustrates a portion of an automated data consolidation module800 including a side view of a third example of a medicationidentification and measurement system 828 and a syringe 806 that can beused with the automated data consolidation module 100, 200 of FIGS. 1and 2. FIG. 9 illustrates a cross-sectional view of the third example ofa medication identification and measurement system 828 and the syringe806 (not shown in cross-section) of FIG. 8, taken along line 9-9. FIGS.8 and 9 are described together.

As shown in FIGS. 8 and 9, a syringe barrel 818 may be centered and heldin place by one or more orienting members, such as compressionpositioning members 842A,B. The compression positioning members 842A, Bmay be urged apart by inserting the syringe barrel 818 there between.Springs 844A-D can compress and create a pressure pushing thecompression positioning members 842A,B against syringe barrel 818. Thecompression positioning members 842A,B shown in FIGS. 8 and 9 are merelyillustrative, and many other sizes, shapes, numbers and locations ofcompression positioning members 842 are anticipated.

Compression positioning members 842A,B may be simple spring 844A-Dactivated devices (e.g., resilient members) as shown in FIGS. 8 and 9 ormay be any mechanism that can expand (e.g., resiliently expand) toaccommodate syringe barrels of various sizes and urge the syringe barrel818 into a centered position within the space defined by a real orimaginary injection portal tube 816. This example shows spring 844A-Dactivated compression positioning members 842A,B but many othermechanical activation mechanisms are anticipated. The compressionpositioning members 842A,B can be elastically deformable, substantiallyelastically deformable, resiliently deformable, include one or moreresilient members.

Other examples of positioning members designed to hold an insertedsyringe 806 in the center of the injection portal 811 and thus orientingthe Luer taper 913 for mating with the injection port 915 areanticipated. Positioning the inserted syringe 806 in the center of theinjection portal 811 allows the machine vision to work from a knowndistance and thus calculations of syringe plunger 948 movement can bevery accurate.

In some examples, instead of the positioning members shown in theexamples of FIGS. 6-9 holding a syringe centrally, the positioningmembers 622A-F or 842A,B can be designed to hold an inserted syringe606, 806 at a known, but off center position in the injection portal611, 811, such as when the injection port 715, 915 (FIGS. 7 and 9) ispositioned off center in the injection portal 611, 811. Any arrangementof at least one positioning member that aligns an inserted syringe at aknown position may be provided.

In some examples, and as shown in FIGS. 4, 6 and 8 the medicationidentification and measurement system 428, 628, 828 of this disclosuremay include one or more “machine vision” cameras 436, 636, 836 thatinput digital images into one or more processors having processingcircuitry 157, 257 as shown and described in FIGS. 1,2, that isprogrammed to analyze machine vision images. In some examples, one ofthe images that the machine vision cameras 436, 636, 836 may “see” is abarcode label 307 on the syringe 406, 606, 806, that has been insertedinto the injection portal 411, 611, 811, for identifying the medicationin the syringe 406, 606, 806. As previously noted, the barcode label 307can identify the brand name and/or generic name of the medication in thesyringe. In some examples, the barcode label 307 also may identify oneor more of the concentration of the medication, the lot number, theexpiration date and other information that may be useful for inventorymanagement.

As shown in FIGS. 4, 6 and 8, the automated data consolidation module400, 600, 800 of this disclosure can include one or more radio frequencyidentification (RFID) interrogation antennas 438, 638, 838 that inputRFID information into a processor, such as processing circuitry 157, 257as shown and described in FIGS. 1 and 2, that is programmed to analyzeRFID data. In some examples, the RFID interrogation antennas 438, 638,838 can interrogate a RFID tag 308 (FIG. 3) attached to the syringe 406,606, 806, that has been inserted into the injection portal 411, 611,811, for identifying the medication in the syringe 406, 606, 806. Insome examples, short range RFID such as near field (NF) or highfrequency (HF) may be advantageous because they may only detect thesyringe 406, 606, 806 that is adjacent to or inside the security systemfor IV medications 400, 600, 800, and not detect the various othermedication syringes that may be sitting on the worktable such as206A-206C in FIG. 2.

As shown in FIGS. 4, 6 and 8 the medication identification andmeasurement system 428, 628, 828 of this disclosure may include a RFIDinterrogator 438, 638, 838. In some examples, the RFID interrogator 438,638, 838 that can include antennas that may be located inside themedication identification and measurement system 428, 628, 828 . In someexamples, the RFID interrogator antennas 438, 638, 838 may be locatedexternal to but proximate the medication identification and measurementsystem 428, 628, 828. As the syringe 406, 606, 806 is brought intoproximity of the medication identification and measurement system, theRFID interrogator 438, 638, 838 can interrogate the RFID tag 308 on thesyringe 406, 606, 806, thereby accurately and reliably identifying amedication for IV administration. In some examples, the RFID tag 308 orother marker may include one or more of: the generic and brand name ofthe drug, the concentration, the lot number, the expiration date, themanufacturer and other important information that may be recorded. Insome examples, the generic and brand name of the drug and theconcentration of the drug can be displayed in the injection section of adisplay such as the display 126, 226 (FIGS. 1, 2).

Machine vision is very accurate for measuring the size of an object at aknown distance or the distance of an object of known size. However, itcannot do both. Therefore, in some examples it is important to know theexact location of a syringe and thus know the distance from the camerato the syringe in order to accurately calculate the distance of themovement of the plunger within the syringe.

Syringes are available in multiple sizes such as 3 cc, 6 cc and 12 cc,each of which is a different diameter. The machine vision processor mustknow both the internal diameter of the barrel of the syringe and thedistance that the syringe plunger moves down the barrel, in order tocalculate the volume of medication injected, unless it has anothersource of information. The machine vision of this disclosure can measurethe diameter of the syringe because in the examples the syringe 406,606, 806 is held at known distance and in a centered location relativeto the machine vision cameras 436, 636, 836. Alternately, the automateddata consolidation module 400, 600, 800 of this disclosure may beprogrammed to know that the particular hospital uses only Monoject®syringes for example and the internal diameter of each Monoject® syringesize may be pre-programmed into the computer. In this case, the machinevision only needs to differentiate 3 cc, 6 cc and 12 cc syringe sizesfrom each other. The machine vision processor can determine the internaldiameter of the barrel of the syringe. In some examples, the syringesize may be included in the information provided by the barcode 307 orRFID 308 (FIG. 3).

In some examples, such as the examples of FIGS. 4-9, the machine visionsystem, including the machine vision camera 436, 636, 836 and theprocessor 157, 257 of FIGS. 1 and 2 (e.g., processing circuitry) inelectrical communication with the machine vision camera 436, 636, 836,can visually detect and determine other geometry information about thesyringe 406, 606, 806 besides the outside diameter, such as determiningthe inside diameter, or the inner or outer length of the syringe. Themedication identification and measurement systems 428, 628, 828 can usethe geometry information to determine the size or type of the syringe406, 606, 806, or can use the geometry information to calculate a volumeof the syringe 406, 606, 806.

In some examples, as the syringe 406, 606, 806 is advanced into theinjection portal 411, 611, 811, the image of the syringe 406, 606, 806entering the injection portal 411, 611, 811 is displayed in real time inan injection section 126 a, 226 a of the display 126, 226 (FIGS. 1 and2). Therefore, the caregiver can watch the syringe 406, 606, 806 advanceand engage with the injection port 515, 715, 915. In some examples, theinjection portal tube 416, 616, 816 or the spring positioning members622A-E or the compression positioning members 842A,B, urge the syringe606, 806 into position to mate with the injection port 715, 915 but theactual connection can also be observed as it is happening by thecaregiver on the display 126, 226. Even though the caregiver is notphysically holding the injection port 515, 715, 915 as they typicallywould, they can watch the engagement of the Luer connector 513, 713, 913with the injection port 515, 715, 915 on a display 126, 226, the view isessentially identical to the thousands of injections that they have madeduring their career. In some examples, the actual image of the syringe406, 606, 806 can be displayed on the display 126, 226, while in otherexamples the data obtained by the camera 436, 636, 836 can be convertedto a representation of the syringe displayed on the display 126, 226.

In some examples, once the syringe 406, 606, 806 is securely connectedto the injection port 515, 715, 915, the caregiver pushes on the plunger446, 646, 846 of the syringe 406, 606, 806, injecting the medicationinto the injection port 515, 715, 915 and IV tubing 520, 720, 920. Thecaregiver can visualize the plunger seal 548, 748, 948 move down thesyringe barrel 418, 618, 818 and can determine the volume of medicationinjected by the graduated markings on the syringe 406, 606, 806. Thus,the engagement of the Luer connector 513, 713, 913 with the injectionport 515, 715, 915 and the injected volume are observed by the caregiveron the display 126, 226 and the traditional method and routine ofinjection is minimally altered by implementing the automated dataconsolidation module 100, 200 including the example medicationidentification and measurement systems 428, 628, 828.

In some examples, the processing circuitry 157, 257 (FIGS. 1 and 2) or acomputer may also simultaneously generate data representing a runningtotal of the volume and dosage of the injected medication and cantransmit the generated data to the display 126, 226 to display volumeand dosage information on the display 126, 226. In some examples, theprocessing circuitry 157, 257 or a computer may also generate its owngraduated scale and transmit the generated graduated scale informationto the display 126, 226 to superimpose the scale on the image of thesyringe 406, 606, 806 or next to the image of the syringe 406, 606, 806,for added visual clarity of the injected volume and dose.

In some examples, the machine vision determination of the injectedvolume may be calculated by multiplying the internal cross-sectionalarea of the syringe (πr²) by the distance that the syringe plungermoves. The radius of the syringe may be determined in one or more ways.For example, the machine vision function may determine that the syringeapproximates a 3 cc or 12 cc syringe and the computer is programmed toknow that the hospital uses a specific brand of syringes and theinternal diameter (radius) of each of these syringe sizes is preciselyknown. (An example of diameter d, radius r is shown in FIG. 5) Anotherexample may require the machine vision camera to measure the outerdiameter of the syringe and then subtract an approximated wall thickness(either measured or known value stored in a memory) from the measureddiameter to determine the internal diameter. In another example, theinternal diameter of the syringe may be supplied to the processingcircuitry 157, 257 or a computer as part of the RFID 308 or barcode 307information. In another example, the machine vision may determine theinner diameter of the syringe by determining an outer diameter of theplunger as viewed through the transparent or semi-transparent syringeand determine the wall thickness, In yet another example, the machinevision may be able to visibly determine the inner diameter or radiusdirectly through the transparent or semi-transparent syringe. Any othersuitable determination, calculation or algorithm may be used todetermine the radius, diameter and injected volume.

In some examples, the machine vision determination of the distance thatthe syringe plunger 446, 646, 846 moves may be by “observing” themovement of the black rubber plunger seal 548, 748, 948 against thevisible scale printed on the syringe 406, 606, 806. In this example, themachine vision can be programmed to recognize the markings on thesyringe 406, 606, 806.

In some examples, the machine vision determination of the distance thatthe syringe plunger 446, 646, 846 moves may be by observing the movementof the black rubber plunger seal 548, 748, 948 relative to a scalecalculated by the processing circuitry 157, 257 (FIGS. 1 and 2). Thegeometrical calculation of the scale that determines the distance thatthe syringe plunger 446, 646, 846 moves may be easiest to determinealong the widest part of the syringe that corresponds with the center C(FIG. 5) of the syringe 406, 606, 806, which is a known distance fromthe machine vision camera 436, 636, 836. Alternatively, thecomputer-constructed scale may be applied to the side of the syringe406, 606, 806 facing the camera 436, 636, 836, if the radius of thesyringe 406, 606, 806 is subtracted from the known distance to thecenter C (FIG. 5) of the syringe 406, 606, 806 in order to calculate thedistance 437 from the machine vision camera 436, 636, 836 to the nearside (e.g., 411A) of the syringe 406, 606, 806.

In some examples, the movement of the black rubber plunger seal 548,748, 948 of the syringe 406, 606, 806 can be clearly identifiable by themachine vision camera 436, 636, 836 and a scale to determine thedistance moved by the plunger 446, 646, 846 can either be “visualized”or constructed by the machine vision computer (e.g., processingcircuitry). Multiplying the distance that the plunger seal 548, 748, 948moves by the known or measured internal diameter d (FIG. 5) of thesyringe 406, 606, 806 and thus cross-sectional area of the plunger seal548, 748, 948, allows the processing circuitry 157, 257 or a computer inelectrical communication with the processing circuitry 157, 257 tocalculate an accurate injected volume. The measured injection volume anddosage may be displayed on the display 126, 226 of the module 101, 201(FIGS. 1 and 2). Without interfering with or changing theanesthesiologists' normal or traditional medication injection routines,an unobtrusive machine vision camera 436, 636, 836 and computer (e.g.,processing circuitry) can “observe” the medication injections andautomatically record them in the EMR.

In some examples, the injected volume of medication may be determined byother sensors or methods. For example, the systems described herein canemploy (e.g., substitute) other sensors such as a non-visual opticalsensor 436A in place of or in addition to the machine vision camera 436,636, 638 described in FIGS. 4-9. For example, a light source can shineon one or more light sensitive elements such as photodiodes, and theposition of the plunger of the syringe can be roughly determined by theobstruction of the light beam by the plunger. Other fluid measurementmethods can have a sensor including adding magnetic material to thesyringe plunger and detecting movement of the plunger with a magneticproximity sensor. Alternatively, fluid flow may be measured with fluidflow meters in the IV fluid stream. These examples are not meant to bean exhaustive list but rather to illustrate that there are alternativetechnologies to machine vision (e.g., sensors), for noncontactmeasurement (e.g., sensing) of fluid flow from a syringe that areanticipated in this disclosure.

In some examples, the injected volume of medication may be determined byother sensors or methods. For example, the systems described herein canemploy (e.g., substitute) other sensors in place of or in addition tothe machine vision cameras for either or both the medication and fluidflows. Other fluid flow sensors anticipated by this disclosure includebut are not limited to: ultrasonic flow meters, propeller flow meters,magnetic flow meters, turbine flow meters, differential pressure flowmeters, piston flow meters, helical flow meters, vortex flow meters,vane flow meters, paddle wheel flow meters, thermal flow meters,semicylindrical capacitive sensor flow meters and Coriolis flow meters.

Securing the injection port 515, 715, 915 within the injection portal411, 611, 811 prevents the caregiver from touching the injection port515, 715, 915. Normally caregivers wear gloves to protect themselvesfrom infectious contaminates from the patient and operating room andtheir gloves are nearly always contaminated. Anything they touch will becontaminated. They typically pick up and hold the IV injection port 515,715, 915 with one hand while inserting the Luer taper connector 513,713, 913 of the syringe 406, 606, 806 into the injection port 515, 715,915. In the process, the injection port 515, 715, 915 is frequentlycontaminated with pathogenic organisms from their gloves that can enterthe patient's blood stream with the next injection, causing seriousinfections. It is therefore advantageous from the infection preventionpoint of view, if the Luer connection and injection can be accomplishedwhile never touching the injection port 515, 715, 915.

In some examples as shown in FIGS. 1 and 2, the automated dataconsolidation module 100, 200 may include an external reader, such asbarcode reader 180, 280 on the module 101, 201 to read a barcode, QRcode or the like for identification. This barcode reader 180, 280 may beused to identify the healthcare provider injecting a medication byreading a barcode or QR code 1186 on the user's ID badge for example(FIG. 11). In some examples as shown in FIGS. 1 and 2, the automateddata consolidation module 100, 200 may include an external RFID reader182, 282 on the module 101, 201. This RFID reader 182, 282 may be usedto identify the healthcare provider injecting a medication by reading anRFID tag 1188 on the user's ID badge 1184B for example (FIG. 11). Insome examples as shown in FIGS. 4, 6 and 8, the automated dataconsolidation module 400, 600, 800 may include an internal RFID reader438, 638, 838 in the module 101, 201. This RFID reader 438, 638, 838 mayalso be used to identify the healthcare provider injecting a medicationby reading an RFID tag on the user's ID badge for example.

It is an important part of the record to know who injected themedication and their identity can be easily verified and documented bythe automated data consolidation module 400, 600, 800 using eitherbarcode, QR code or RFID. Other identification technologies are alsoanticipated.

FIG. 10 illustrates an example injection port cassette 1054 that can beused with the automated data consolidation module 100, 200 of FIGS. 1and 2, as detailed in FIGS. 5, 7 and 9. As shown in FIG. 10, theinjection port 1015 may be mounted on an injection port cassette 1054 inorder to make the attachment to the automated data consolidation module100, 200, 400, 600, 800 easier and more secure. The injection portcassette 1054 may be a piece of molded polymer or plastic onto which theinjection port 1015 and IV tubing 1020 may be attached. The injectionport cassette 1054 may be shaped and sized to fit into a slot in theautomated data consolidation module 100, 200, 400, 600, 800. When theinjection port cassette 1054 is fit into a slot in the automated dataconsolidation module 100, 200, 400, 600, 800, the injection port 1015can be positioned substantially in the center of the injection portal411, 611, 811 for mating with the Luer tapers 513, 713, 913. Theinjection port cassette 1054 can also be configured to be removed intactfrom the automated data consolidation module 400, 600, 800 so that thepatient can be transferred and the IV tubing 1020 can be moved with thepatient and continue to operate normally.

In some examples, and as shown in FIG. 10, the injection port cassette1054 can include an IV bypass channel 1056 in the IV tubing 1020. The IVbypass channel 1056 can allow the IV fluids to flow unencumbered by themedication injection apparatus. The injection port cassette 1054 caninclude a medication channel 1058 in the IV tubing 1020 and, themedication channel 1058 may include one or more stop-flow clamps1060A,B. The one or more stop-flow clamps 1060A,B may be activated bythe automated data consolidation module100, 200, 400, 600, 800 if amedication error is identified. The one or more stop-flow clamps 1060A,Bmay be powered by one or more electromechanical solenoids that squeezethe IV tubing in the medication channel 1058 flat, obstructing the flow.Other electromechanical flow obstructers are anticipated.

In some situations, such as when administering a drug to a patientallergic to that drug, or administering potent cardiovascular drugs to apatient with normal vital signs, or administering a drug with a likelymistaken identity, the computer, such as processing circuitry 157, 257(FIGS. 1 and 2) for the automated data consolidation module 100, 200,400, 600, 800 of this disclosure can automatically activate thestop-flow clamps 1060A,B to compress the medication channel 1058 tubingupstream and/or downstream from the injection port 1015. Compressing theIV tubing both upstream and downstream from the injection port 1015prevents the injection of any medication into the IV tubing 1020. Analert to the adverse condition of the injection may be displayed ondisplay 126, 226 where the stop-flow condition can be over-ridden by theoperator touching a manual override switch on the display 126, 226 orthe module 101, 201, if the injection was not erroneous. While thestop-flow can occur in the medication channel 1058, the IV fluid flowcan continue normally in the parallel bypass channel 1056.

The stop-flow clamps 1060A,B can allow the processing circuitry 157, 257(e.g., processor, hardware processing circuitry) of the automated dataconsolidation module 100, 200, 400, 600, 800 to not only warn theoperator of a pending medication error, but physically prevent theinjection. Perhaps equally as important is that the stop-flow clamps1060A,B can be quickly released by the operator touching a manualoverride switch in the event that the apparent error was in fact aplanned event or otherwise desired by the operator.

In some examples, a part of the automated data consolidation module 100,200, 400, 600, 800 can include the ability for the computer (e.g.,machine) to know the patient's medical history, medication orders, vitalsigns, current medications, medication orders and other importantinformation about that patient. In some examples, the medication insyringe 306 can be identified by RFID tag 308 (FIG. 3) and is detectedby RFID interrogator 438, 638, 838 as the syringe 306 enters injectionportal 411, 611, 811. The processing circuitry (e.g., 157, 257) of theautomated data consolidation module 100, 200, 400, 600, 800 cancross-reference the proposed injection to the patient's medical history,medication orders, vital signs, current medications and other importantinformation about that patient, providing a safety “over-watch” guardingagainst medication errors. In some examples, the processing circuitry(e.g., 157, 257) of the automated data consolidation module 100, 200,400, 600, 800 may include algorithms and/or “artificial intelligence”that can provide alternative medication suggestions based on patient'smedical history, medication orders, vital signs, current medications andother important information about that patient.

In some examples, the EMRs that were created by the automated dataconsolidation module 100, 200, 400, 600, 800 of this disclosure canprovide accurate and temporally correlated information about therelationship between any injected medication and the resultingphysiologic response. This is uniquely accurate dose-response data. Insome examples, the EMRs that were created by the automated dataconsolidation module 100, 200, 400, 600, 800 for hundreds of thousandsor even millions of patients, may be aggregated and analyzed as “bigdata.” The “big data” from these EMRs may be used for a variety ofpurposes including but not limited to medical research, patient andhospital management and the development of “artificial intelligence”algorithms that can provide alternative medication suggestions. Ongoing“big data” from more and more EMRs can be used to continually improveand refine the “artificial intelligence” algorithms, much like the“artificial intelligence” algorithm development process being used todevelop self-driving vehicles. These “artificial intelligence”algorithms can be used to provide automated (“self-driving” or“partially self-driving”) anesthesia during surgery or automatedmedication delivery.

FIG. 11 illustrates a plan view of an example of healthcare provider IDbadges 1184A and 1184B that can be used with the system of FIGS. 1 and2, in accordance with at least one example. In some examples, the “chainof custody” may begin by electronically identifying the healthcareprovider as the drugs are checked out from the pharmacy or Pixismedication dispenser. In some examples and as shown in FIG. 11, eachprovider can have a personalized RFID tag 1186, attached to theirhospital ID badge 1184A. In some examples each provider can have apersonalized barcode 1188, attached to their hospital ID badge 1184B forexample. When the drugs are checked out, the personalized RFID tag 1186may be read by an RFID interrogator or the personalized barcode 1188read by a barcode reader in the pharmacy and the ID of the providerchecking the drugs out may be noted in the hospital's computer and/orthe processing circuitry 157, 257 (FIGS. 1 and 2) for the automated dataconsolidation module 100, 200, 400, 600, 800 (FIGS. 1, 2, 4, 6 and 8).The specific RFID 308 or barcode 307 (FIG. 3) identification of theinjectable drug may also be recorded before the drug leaves the pharmacyand that information may be transmitted to the processing circuitry 157,257 of the automated data consolidation module 100, 200, 400, 600, 800of this disclosure. In some examples, instead of an RFID tag 1186 andRFID reader, other provider identification information and sensors foridentifying the provider can be used, such provider identificationinformation may include: a barcode, a QR code with the sensor being ableto read such codes. In other examples, the sensor can include a retinalscanner, fingerprint reader or a facial recognition scanner thatidentifies the provider by personably identifiable information (e.g.,provider identification information) may be used.

In some examples, when the provider arrives at the patient's bedside,the provider may be identified by their ID badge 1184 A,B. In someexamples, an ID badge 1184A that has an RFID tag 1186 may be read byRFID reader 182, 282 (FIGS. 1 and 2) that can be located on theautomated data consolidation module 100, 200, 400, 600, 800 or by RFIDreader 438, 638, 838 (FIGS. 4, 6 and 8) located inside the automateddata consolidation module 100, 200, 400, 600, 800 (FIGS. 1, 2, 4, 6 and8). In some examples, an ID badge 1184B that has a barcode 1188 may beread by barcode reader 180, 280 that can be located on the automateddata consolidation module 100, 200, 400, 600, 800. In some examples, aretinal scanner may be located on or near module 101, 201 in order topositively identify the provider by their retinal vasculature. Otherscanners including but not limited to facial recognition scanning arealso anticipated in order to positively identify the provider doing theinjection in order to automatically document this information to an EMRor other record.

In some examples, the provider's photograph may be taken by camera 190,290 (FIGS. 1 and 2) for further identification before allowing theinjection of scheduled drugs. In some examples, the camera 190, 290 maybe triggered, such as by processor 157, 257 (FIGS. 1 and 2), when asyringe e.g., 306B filled with a scheduled drug such as a narcotic isidentified as it enters the injection portal 411, 611, 811 and the RFIDinterrogator 438, 638, 838 interrogates the RFID tag 308 (FIG. 3) or themachine vision camera 436, 636, 836 reads the barcode 307 (FIG. 3) onthe syringe 306A. Non-scheduled medications may not need the addedsecurity of a photograph.

In some examples as shown in FIGS. 1 and 2, the automated dataconsolidation module 100, 200 of this disclosure may include a remotemonitor with display 187, 287. The remote monitor may include a wired orwireless connection to the automated data consolidation module 100, 200and may display some or all of the information shown on the electronicrecord display 126, 226, or other information generated by the automateddata consolidation module 100. For example, the processing circuitry157, 257 can be in electrical communication with the remote display 187,287 and the processing circuitry 157, 257 can send instructions to theremote display 187, 287 to display the generated information.

The remote monitor may be in the next room or miles away. The remotemonitor may allow remote supervision of healthcare delivery. Forexample, anesthesiologists frequently supervise up to four surgicalanesthetics at once, each being delivered by a nurse anesthetist. Inthis case, the anesthesiologist carrying a wireless remote monitor 187,287 can have real-time data on each case under their supervision.Similarly, a nurse anesthetist working in a rural hospital may besupervised by an anesthesiologist who is 50 miles away.

In some examples, the remote monitor with display 187, 287 can create arecord for billing. For example, when an anesthesiologist is supervisingmultiple anesthetics at once, the payers may dispute the involvement ineach case and refuse to pay. The remote monitor with display 187, 287may include an RFID reader that documents the close proximity of an RFIDtag on the anesthesiologist's ID badge. Any other type of proximitysensor may be used in place of the RFID tag, including but not limitedto GPS location sensing. Documenting that the anesthesiologist wascarrying the remote monitor with display 187, 287 throughout the time ofthe surgery is very good evidence that the anesthesiologist was activelyparticipating in the care of the patient.

In some examples, the remote monitor with display 187, 287 allowslong-distance medical consultation. For example, an expert at the MayoClinic could consult with a physician halfway around the world in Dubai,responding to real-time patient data displayed on the remote monitorwith display 187, 287.

FIG. 14 illustrates a side view of an example IV fluid identificationand measurement system 1430 that can be used with the systems of FIG.1-9, and the injection port cassette of FIG. 10. Some aspects of FIGS.1-14 are described together, however, the examples are merelyillustrative and the features can be used in any suitable combination.In some examples, the automated data consolidation module 100, 200 ofthis disclosure includes a system for automatically measuring andrecording the administration of IV fluids. To accomplish this, as shownin FIGS. 1, 2 and 14, the automated data consolidation module 100, 200can include an IV fluid identification and measurement system 130, 230,1430. In some examples, the IV fluid identification and measurementsystem 130, 230, 1430 can be mounted onto module 101.

Alternately, the IV fluid identification and measurement system 130,230, 1430 may be mounted to an IV pole 105 or racking system independentfrom the module 101. The system for automatically measuring andrecording the administration of IV fluids is not limited to use inanesthesia or in the operating room, but has applicability for usethroughout the hospital and other health care settings, including butnot limited to the ICU, ER, wards, rehabilitation centers and long termcare settings. In some examples, aspects of the IV fluid identificationmeasurement system 130, 230, 1430 can be provided alone or together withother features of the automated data consolidation module 100, 200including the medication identification and measurement system 128, 228.

In some examples, the IV fluid identification and measurement system130, 230, 1430 may be configured to accommodate one or more bags of IVfluid 132, 232A,B and 1432A,B. Each bag of IV fluid can include a dripchamber 134, 234A,B and 1434A,B and IV tubing 120, 220A,B and 1420A,B.IV flow rates may be controlled with the traditional manually operatedroller clamp that variably pinches the IV tubing 120, 220A,B and 1420A,Bto control or even stop the flow of IV fluids. In some examples, IV flowrates may be controlled with the automatically operatedelectromechanical flow rate clamps 1478 that variably pinch the IVtubing 1420A to control or even stop the flow of IV fluids. Theautomatically operated electromechanical flow rate clamps 1478 may becontrolled by the processing circuitry 157, 257, such as an electronicanesthetic record computer in module 101 or by any other suitableprocessor including hardware processing circuitry that is in electricalcommunication with the IV fluid identification and measurement system130, 230, 1430 and/or is located within the IV fluid identification andmeasurement system 130, 230, 1430.

In some examples, the IV fluid identification and measurement system130, 230, 1430 is configured to automatically measure and record theadministration of IV medications and fluids. The system 130, 230 caninclude one or more of a barcode reader and an RFID interrogator (suchas 1436A,B) for accurately and automatically identifying a fluid for IVadministration. Because of the close proximity to the adjacent bags,barcode identification may be preferable in order to prevent an RFIDinterrogator from reading the RFID tag on a neighboring bag. In someexamples, as shown in FIG. 14, one or more barcode labels 1405A,B may beapplied to the IV bags 1432A,B in a location where they can be read by asensor such as a barcode reader or machine vision camera 1436A,B, oranother machine vision camera located in a suitable position to read thebarcode. In some examples, a dedicated barcode reader or a machinevision camera may be positioned adjacent the barcode label 1405A,Blocation, specifically for reading the barcode label 1405A,B.

In some examples, the drip chamber 234A,B and 1434A,B of the IV set canbe positioned adjacent the one or more machine vision cameras 1436A,B.In some examples, a standard background 1468A,B may be positioned on theopposite side of the drip chamber 1434A,B from the machine visioncameras 1436A,B. The standard background 1468A,B may be a plainbackground or may be an advantageous color, pattern, color design orillumination that highlights each of the falling drops, for easieridentification by the processing circuitry 157, 257 (e.g., 1502, FIG.15). The machine vision software including instructions can be stored onone or more machine-readable mediums (such as 1522 in FIG. 15) that whenimplemented on hardware processing circuitry (including but not limitedto processing circuitry 157, 257) or in electrical communication withthe system, can perform the functions described herein. An example ofsuch electrical connection is shown by the connection of processor 1502with mass storage 1516 in FIG. 15.

In some examples, the processing circuitry 157, 257, 1502 can beconfigured to look for (e.g., sense, monitor, detect) a fluid meniscus1464A,B in the drip chamber 1434A,B. In this case “seeing” a fluidmeniscus 1464A,B indicates that there is fluid in the drip chamber1434A,B and therefore the IV bag 1432A,B is not empty, and air is notinadvertently entering the IV tubing 1420A, 1420B.

In some examples, if the IV fluid identification and measurement system130, 230, 1430 fails to “see” a fluid meniscus 1464A,B meaning that thedrip chamber 1434A,B is empty and thus the IV bag 1432A,B is empty,stop-flow clamps 1460A,B can be automatically activated. For example,processing circuitry 157, 257 can send an instruction to activate thestop-flow clamps 1460A,B to compress the IV tubing 1420A,B in order toprevent air from entering the IV tubing 1420A,B. In some examples, theempty IV bag 1432A,B condition detected by the processing circuitry 157,257 can cause an alert to be displayed to the caregiver on theanesthetic record display 226, such as by sending an instruction to thedisplay.

The combination of the machine vision camera 1436A,B in electricalcommunication with processing circuitry (e.g., 157, 257, FIGS. 1 and 2)that executes instructions stored on a machine readable medium can countthe number of drops of fluid per unit of time in a drip chamber 1434A,Bto calculate or to estimate the flow rate of an IV. The size of the dripchamber 1434A,B inlet orifice determines the volume of liquid in eachdrop. The inlet orifices of standard drip chambers are sized to createdrops sizes that result in 10, 12, 15, 20, 45 and 60 drops per ml. Givena particular drop volume (size), 10 drops per ml for example, the system130, 230, 1430 (e.g., via sensors, processing circuitry and machinereadable medium) can count the number of drops falling in a known periodof time and use that data to calculate or to estimate the flow rate. Ifthese estimates were attempted by a human, they may be less accurate athigher flow rates (higher drop counts) because the drops are so fast, itcan be difficult to count the drops. Eventually, at even higher flowrates the individual drops become a solid stream of fluid and the flowrate cannot be visually estimated.

In some examples, the IV fluid identification and measurement system130, 230, 1430 is configured to look for falling drops of fluid 1462A,Bwithin the drip chamber 1434A,B. When drops 1462A,B have beenidentified, the machine vision system (e.g., machine vision camera 1436Aor 1436B operably coupled to processing circuitry 157, 257) may firstmeasure the diameter of the drop 1462A,B to determine which of thestandard drop sizes or volumes it is counting. Most hospitalsstandardize on several infusion set sizes, 10, 20 and 60 drops per ccfor example. Therefore, when these limited choices of infusion setbrands and sizes have been programmed into the computer, the machinevision system only needs to differentiate between these choices, whichis much easier than accurately measuring the diameter of the drops.Unlike the human eye, the machine vision can accurately count thefalling drops even at high flow rates to calculate an IV fluid flowrate.

In some examples, the machine vision system, including the machinevision cameras 1436A,B and instructions 1524 (e.g., software) stored ona machine readable medium 1522 and implemented by hardware processingcircuitry 157, 257 does not “see” falling drops 1462A,B. In thissituation, either the fluid is flowing in a steady stream that is notidentifiable or the fluid has stopped flowing. In some examples, thesetwo opposite conditions can be differentiated by inserting a floatingobject 1466A,B (hereinafter, “float”) into the drip chambers 1434A,B. Insome examples, the float1466A,B may be a ball-shaped float 1466A,B. Insome examples, the float may be patterned or multi-colored to moreeasily identify movement or spinning of the float. In some examples, ifthe machine vision system cannot identify falling drops 1462A,B, it thenlooks to the float 1466A,B for additional information. If the float1466A,B is not moving or spinning, the fluid flow has stopped. If thefloat 1466A,B is moving or spinning and drops cannot be identified, thefluid is flowing in a steady stream and the flow rate cannot be measuredby machine vision. In this situation, the system can determine fluidflow using an alternate method.

In some examples, the IV fluid identification and measurement system130, 230, 1430 may be configured to accommodate one or more bags of IVfluid 132, 232A,B, 1432A,B and each of these IV bags may be hanging froman electronic IV scale 1472A,B (e.g., a weight, a physicalcharacteristic sensor). The electronic IV scale 1472A can measure thecombined weight of the IV bag and fluid 1432A, the drip chamber 1434Aand the IV tubing 1420A. The electronic IV scale 1472B can measure theweight or combined weight of one or more of the IV bag and fluid 1432B,the drip chamber 1434B, the IV tubing 1420B and a pressure infuser 1474.In both of these examples, the electronic IV scale 1472A,B canaccurately measure the change in combined weight that occurs due to thedrainage of the IV fluid from the IV bag 1432A,B. The change in weightper unit time can be converted to flow rates by processing circuitry157, 257 in electrical communication with the electronic IV scale 1472A,1472B, for example, by the processing circuitry 157, 257, 1502 anddisplayed on the electronic record display 126, 226.

In some examples, the calculated flow rates for each IV bag 1432A,B mayalso be displayed on one or more digital flow-rate displays 1476A,Bmounted on the IV fluid identification and measurement system 1430. Thedigital flow-rate displays 1476A,B may be small LED or LCD displays thatconveniently tell the operator the flow rate while they are manuallyadjusting the flow rate near the IV bags 1432A,B and drip chambers1434A,B. The digital flow-rate displays 1476A,B are particularlyconvenient when the IV fluid identification and measurement system 130,1430 is a free standing entity mounted on an IV pole 105 for examplewhile being used on the ward or ICU.

In some examples, when the falling drops 1462A,B cannot be detected andyet the floats 1466A,B are moving or spinning, the fluid is determinedto be flowing in a steady stream and the flow rate cannot be measured bymachine vision. In this case the electronic record computer mayautomatically query the change in weight per unit time as measured bythe electronic IV scale 1472A,B to determine the IV flow rate. At highflow rates, the change in weight per unit time as measured by theelectronic IV scale 1472A, B will most likely be more accurate thancounting drops, in determining the IV flow rate.

The IV flow rate as determined by the change in weight per unit time canalso be compared to the IV flow rates determined by counting drops toverify the accuracy of each method. Without interfering with or changingthe healthcare providers normal or traditional IV routines, anunobtrusive machine vision camera and computer can “observe” the IV flowrates and automatically record them in the EMR.

The module or automated EMR system 101, 201 of this disclosure maycapture anesthetic event data but it must be noted that the sametechnologies described herein for capturing anesthetic event data can beused throughout the hospital or outpatient health care system to captureand record medication administration, IV fluid administration, vitalsigns and patient monitor inputs, provider events and other data.Non-operating room heath care locations are included within the scope ofthis disclosure. While this disclosure focuses on the totality offunctions offered by module 101, 201, each of the individual functionscan be offered independently of module 101, 201.

The use of the term Electronic Anesthetic Record (EAR) as defined hereincan include any memory such as an electronic surgical record (ESR), oran electronic medical record (EMR), or an electronic health record(EHR),and is not limited to anesthetic or surgical applications. Aspectsof the modules 101,201 described herein can also be employed inrecovery, hospital room and long-term care settings.

In an example, the module 201 of FIG. 2, can include a housing 299having a lower section 299A and a tower-like upper section 299B, whereinthe lower section 299A can be configured to house unrelated wasteheat-producing electronic and electromechanical surgical equipment, andwherein the tower-like upper section 299B can be located on top of thelower section 299A. The module 201 can also include a cowling 299Cexternal or internal to the housing 299 that substantially confineswaste heat generated by the unrelated waste heat-producing electronicand electromechanical surgical equipment. In addition, the module 201can include a system for monitoring the administration of one or more IVmedications and fluids 228, 230. As shown in the combination of FIGS. 2,4, 5 and 14, the system 228, 230 can include any of: a barcode 436reader or an RFID 438 interrogator configured to identify the one ormore IV medications or fluids; a machine vision digital camera 436 tocapture an image of one or more of a syringe 406 or a drip chamber1434A,B; processing circuitry 257 operably coupled to the barcode reader436 or the RFID interrogator 438 (or the machine vision digital camera436) to receive the identity of the one or more IV medications orfluids, the processing circuitry 257 operably coupled to the machinevision digital camera 436 to receive the captured image and determine avolume of medication administered from the syringe or fluid administeredfrom an IV bag based on the image; and a display 226 operably coupled tothe processing circuitry 257, the display 226 configured to receiveinstructions from the processing circuitry 257 to output the identityand determined volume of medication administered from the syringe orfluid administered from an IV bag.

As described herein, “dose/response” can be a useful medical process.Dose-response includes giving something to the patient (a medicine forexample) or doing something to the patient (mechanical ventilation orsurgery for example)—the “dose”, and then observing the patient's“response.”

Healthcare data in general is manually entered, not automated.Healthcare data in general is also not granular (e.g., beat-by-beat,second-by-second, continuous) but rather very intermittent (e.g., every15 minutes, every 4 hours, every day etc.). In general, most data in theEMR are simply data that was on the old paper record, manually enteredinto the EMR.

Manual data entry of dose events, whether they are reporting medicationinjections, fluid infusions or changes in the ventilator settings or anyother “dose events” are laborious, distracting and universally dislikedby healthcare providers. Perhaps more important is that the dose eventsare not accurately recorded. First, there are innocent errors inrecording. Second, there are omissions that may be innocent or for lackof time or due to provider sloppiness. Finally, there is no temporalcorrelation between a dose event and a related response event becausethe dose event is most likely manually recorded minutes to hours afterthe dose event occurred (if it is recorded at all).

At this date, the only data that is automatic and timely recorded is the“response” data provided by the physiologic monitors. Even the monitorresponse data is usually not recorded beat-by-beat but ratherintermittently recorded every 5 minutes or 30 minutes or 4 hours forexample—just as it was historically recorded on the paper record. Theremainder of the response data must be manually entered into the recordand thus suffers from the same limitations as noted above for themanually entered dose events. As a result, with current EMRs the doseand response data cannot be temporally correlated with any accuracy,vastly reducing the analytical and predictive value of the electronicdatabase and record. The systems described herein provide a technicalsolution to a technical problem. Furthermore, the benefits achieved bythe technical solutions of this disclosure exceed what can beaccomplished by manual processes.

In some examples, the automated data consolidation module 100,200 ofthis disclosure includes systems and methods for constructing granular(beat-by-beat, second-by-second) anesthetic, surgical and patientrecords that include both “dose” events—the things that are given ordone to the patient (inputs from medication injection and fluidmonitors, various support equipment and machine vision “observations”for example) and “response” events (inputs from electronic physiologicmonitors, various measurement devices and machine vision “observations”for example). In some examples, the automated data consolidation module100,200 of this disclosure automatically enters both dose and responseevents into the electronic record. In some examples, the dose andresponse events of this disclosure are continually recorded, creating avery granular record. Electronic memory has gotten so inexpensive thatvast amounts of data can be practically recorded for analysis at a latertime. In some examples, the dose and response event data of thisdisclosure that do not change very fast or frequently, may becontinually recorded by the processing circuitry 157,257 butintermittently recorded in the EMR to reduce the size of the record.

In some examples, the automated data consolidation module 100,200 ofthis disclosure automatically enters both dose and response events intothe electronic record and temporally correlates the dose and responseevents when they are recorded. Temporally correlating the dose andresponse data can be accomplished by recording dose and response data inparallel, adding integrated or external time stamps to the dose andresponse data or any other suitable method of temporally matching thedata sets.

In an example, the module 201 of FIG. 2, can include a housing 299having a lower section 299A and a tower-like upper section 299B, whereinthe lower section 299A can be configured to house unrelated wasteheat-producing electronic and electromechanical surgical equipment, andwherein the tower-like upper section 299B can be located on top of thelower section 299A. The module 201 can also include a cowling 299Cexternal or internal to the housing 299 that substantially confineswaste heat generated by the unrelated waste heat-producing electronicand electromechanical medical equipment. In some examples, theelectronic and electromechanical medical equipment may be mounted on themodule 201,101. In some examples, the electronic and electromechanicalmedical equipment may be in electrical communication with the module201,101. In some examples, the electronic and electromechanical medicalequipment may be wireless communication with the module 201,101.

In some examples, most of the electronic and electromechanical medicalequipment that are in, on or connected to the module are eitherparticipants in dose events (something given to or done to the patient)or participants in response events (measuring or observing the patient'sresponse). The physical location of the electronic and electromechanicalmedical equipment in or on the automated data consolidation module100,200 of this disclosure, makes collecting data from the variousequipment very efficient.

Unfortunately, most medical equipment available today does not produce adigital output signal that reports its operating settings andmeasurements in a recordable format. In some examples, the automateddata consolidation module 100,200 allows rules to be applied to thevarious medical equipment that is housed within the module, mounted onthe module, or is in electrical communication with or in wirelesscommunication with the module. For example, the hospital or owner of theautomated data consolidation module 100,200 could require that that allequipment in or on the module produce digital data reflecting theequipment's operating parameters and sensor inputs. Equipmentmanufacturers would have a business interest in adding digital outputsto their equipment if they want to sell their equipment to thathospital.

At this date there are few data standards for medical equipment and eachmanufacturer uses any data format that they choose. In some examples,the hospital or the owner of the “big data” database or the owner of theautomated data consolidation module 100,200 could require that that allequipment in or on the module produce data in prescribed data standardsand protocols so that translation of the data is not necessary. In someexamples, the data standards would document the expectations for one ormore of: format, definition, structuring, tagging, transmission,manipulation, use and management of the data. In some examples, the datastandard defines entity names, data element names, descriptions,definitions, and formatting rules. Translating data from one datastandard to another introduces the possibility of error. Therefore,producing data for input to the processing circuitry 157,257 in theprescribed standards and formats minimizes the opportunity for error byavoiding data translation. However, in some examples, the processingcircuitry 157,257 of the automated data consolidation module 100,200includes software for translating data when necessary.

In some examples, the hospital or the owner of the “big data” databaseor the owner of the automated data consolidation module 100,200 couldrequire that all equipment in or on the module produce data to aprescribed data model that includes all relevant input record fields forthat specific type of equipment. When data is used in “big data”databases, it is very common that there are missing data. The automateddata consolidation module 100,200 of this disclosure can minimizemissing data by using prescribed data models for each type of equipmentand requiring that the data fields be filled. In some examples, the datastandard or data model also prescribe the format of the data in eachdata field. Labeling the fields with consistent labels helps when AIanalysis is done on the “big data.”

In some examples, the hospital or the owner of the “big data” databaseor the owner of the automated data consolidation module 100,200 couldrequire that that all equipment in or on the module produce datainstantly. In some examples, instant data production is desirable sothat the processing circuitry 157,257 can apply a time stamp or othertime designation to the data so that the data can be temporallycorrelated with other data. Instant data production also allows streamprocessing to be performed on the data for real-time feedback.Automating data input is far superior to manual data input for manyreasons. One of the important reasons is that automated input can acceptand record high volume, instantaneous data production allowing temporalcorrelations between dose event data and response event data.

In some examples, consistent data standards, consistent data formatting,consistent data fields, consistent data labeling and time stamping,produces data for a “big data” database that is most usable for AI andML analysis and research.

In some examples, consistent data standards, consistent data formatting,consistent data fields also produce data that is more protectable fromthe cyber security point of view. Having inputted structured andsemi-structured data with consistent data standards, consistent dataformatting, consistent data fields helps the processing circuitry157,257 recognize inputted data that could be viral or malicious. Insome examples, the processing circuitry 157,257 and software of theautomated data consolidation module 100,200 can provide front lineaccess control and firewall protection, preventing corrupt data fromentering the system and being transferred to either the EMR or the “bigdata” database. In some examples, the processing circuitry 157,257 andsoftware include one or more of access control software, firewallsoftware, antivirus software, anti-malware, anti-spyware, anti-tampersoftware, anti-subversion software and other cyber security software.

If the thousands of monitors and other pieces of medical equipment in ahospital inputted their data directly to the EMR, it would be verychallenging to provide cyber security on all of those inputssimultaneously. In some examples, since the automated data consolidationmodule 100,200 is receiving data inputs from only 2-30 pieces of medicalequipment and sensors, cyber security is much more manageable.Therefore, it may be advantageous to use the processing circuitry157,257 and software of the automated data consolidation module 100,200to provide cyber security on the inputted data. When the inputted datais known to be secure, it can be freely transferred to the EMR or “bigdata” database without fear of corrupting the system.

In some examples, the hospital or the owner of the “big data” databaseor the owner of the automated data consolidation module 100,200 couldrequire that that all equipment in or on the module produce datacontinuously. One of the “V's” of “big data” is volume—the more data thebetter. Therefore, in some examples, data and measurements that arechanging may be recorded continuously to produce the most data “volume”for that data set. In some examples, the processing circuitry 157,257and software can do data “filtering” in the presence of large size datato discard information that is not useful for healthcare monitoringbased on defined criteria. This may include for example, intermittentlyrecording data that changes slowly such as the patient's temperature,rather than continuously recording. In some examples, when the data isnot changing or changing very slowly, for example the settings on apiece of equipment or the patient's temperature, the processingcircuitry 157,257 may record the data into the EHR or “big data”database, intermittently for efficiency, without losing any “volume.”

In some examples, the processing circuitry 157,257 and software can dodata “cleaning” such as normalization, noise reduction and missing datamanagement. “Sensor fusion” is a technique that may be utilized tosimultaneously analyze data from multiple sensors, in order to detectand discard erroneous data from a single sensor. In some examples, theprocessing circuitry 157,257 and software can be used in many other waysto cleanse, organize and prepare the input data. Data “filtering” anddata “cleaning” prepare the data to enhance the reliability of datamining techniques. Processing raw data without preparation routines mayrequire extra computational resources that are not affordable in a “bigdata” context.

In some examples, the processing circuitry 157,257 and software execute“stream processing” for applications requiring real-time feedback. Insome examples, streaming data analytics in healthcare can be defined asa systematic use of continuous waveform and related medical recordinformation developed through applied analytical disciplines, to drivedecision making for the patient care. In some examples, as shown in FIG.25, streaming healthcare data 2500 may start with fast data ingestion2502 which may include continuous waveforms such as EKG or pulseoximetry or other non-waveform data such as blood pressure 2504. Thenext step in the process is situational and contextual awareness 2506,where the processing circuitry 157,257 and software correlate and enrichthe data set with data from the EHR, patient history, labs, allergiesand medications and other data 2508. The next step in the process issignal processing and feature extraction 2510, where advanced analytics2512 consume relevant data to produce insights. The final step in theprocess is producing actionable insight 2514, which can include clinicaldecision support and alarms 2516. In some examples, it is advantageousto regulate the access of incoming data with access control and firewallsoftware 2518.

In some examples, when the objective is to deliver data to a “big data”database, the data must be pooled. Data in the “raw” state needs to beprocessed or transformed. In a service-oriented architectural approach,the data may stay raw and services may be used to call, retrieve andprocess the data. In the data warehousing approach, data from varioussources is aggregated and made ready for processing, although the datais not available in real-time. The steps of extract, transform, and load(ETL) can be used to cleans and ready data from diverse sources. In someexamples, it is most efficient for the processing circuitry 157,257 andsoftware of the automated data consolidation module 100,200 of thisdisclosure, to process and transform the raw data before sending it tothe “big data” database.

In some examples, an applied conceptual architecture of big dataanalytics 2600 is shown in FIG. 26. Data from multiple sources 2602 canbe inputted into the processing circuitry 157,257 and software of theautomated data consolidation module 100,200. Big Data sources 2602 anddata types include but are not limited to: Machine to machinedata—readings from remote sensors, meters and other vital signs devices;big transaction data—health care claims and other billing records;biometric data—x-ray and other medical images, blood pressure, pulse andpulse-oximetry readings, and other similar types of data;human-generated data—unstructured and semi-structured data such as EMRsand physician notes. In some examples, it is advantageous to regulatethe access of incoming data with access control and firewall software2614.

In some examples, the processing circuitry 157,257 and software of theautomated data consolidation module 100,200 take the data and performthe steps of extract, transform, and load (ETL) 2604 to cleans and readythe data from diverse Big Data sources 2602. Once the data is processedor transformed, several options are available for proceeding with bigdata. A service-oriented architectural approach combined with webservices (middleware) is one possibility 2606. The data stays raw andservices are used to call, retrieve and process the data. Anotherapproach is data warehousing 2608 wherein data from various sources isaggregated and made ready for processing. With data warehousing 2608 thedata is not available in real-time.

Data transformation 2604 via the steps of extract, transform, and load(ETL), cleanses and readies the data from diverse sources 2602.Depending on whether the data is structured or unstructured, severaldata formats can be inputted to a big data analytics platform 2610. Bigdata is then organized into big data analytics applications 2612 such asqueries, reports, OLAP and data mining, so that it can be useful.

In some examples, with “big data” database data, the processingcircuitry 157,257 and software may execute “batch processing,” analyzingand processing the data over a specified period of time. Batchprocessing aims to process a high volume of data by collecting andstoring batches to be analyzed in order to generate results. In someexamples, the processing circuitry 157,257 and software can serve as a“node” in batch computing, where big data is split into small piecesthat are distributed to multiple nodes in order to obtain intermediateresults. Once data processing by nodes is terminated, outcomes will beaggregated in order to generate the final results. In some examples,stream processing and batch processing may be both used, either inparallel or sequentially.

In some examples, the automatically entered, temporally correlated doseand response events in the patient's electronic medical record (EMR) maybe analyzed by artificial intelligence (AI) and/or machine learning (ML)software in the processing circuitry 157,257 of the module for immediateadvisory, alerts and feedback to the clinician. The patient's own datacan be analyzed by AI algorithms that were derived from general medicalknowledge and research or may be analyzed by AI algorithms that werederived from AI analysis of the pooled database of many patients, suchas machine learning (ML) or a combination of the two.

In some examples, the automatically entered, temporally correlated doseand response events in the patient's electronic record may be pooledwith the records of other patients in a database that can be analyzedwith artificial intelligence and machine learning software stored on amemory and executed by processing circuitry. This analysis can occur atthe time of temporal correlation or at a later time. This type ofanalysis is sometimes known as “big data.” The pooled data (e.g.,aggregated data, compiled data, such as collective data of a group ofpatients, collective data for a specific patient over different periodsof time, doses, responses, medications, providers) may be stored onproprietary servers or may be stored in “the cloud.” In any event, the“big data” database of this disclosure is complete and granular(compared to current healthcare databases) because it was automaticallyentered and at least some of the data is continually recorded. The “bigdata” database of this disclosure includes both dose events and responseevents (compared to current healthcare databases that are weightedtoward response events) and the dose and response events are temporallycorrelated (compared to current healthcare databases). Temporalcorrelation can include time matching different sets of data streams.

“Big data” has been characterized by “four Vs”: Volume, Velocity,Variety and Veracity. In some examples, the automated data consolidationmodule 100,200 of this disclosure addresses each of thesecharacteristics in order to optimize the acquired data for “big data”use and analysis.

Volume—The more data you have the better it is. The automated dataconsolidation module 100,200 of this disclosure maximizes data volume bycollecting data not only from the physiologic monitors but also all ofthe OR equipment and other dose events including machine vision videoobservations. Further, the automated data consolidation module 100,200can provide either continuous recordings or intermittent recordingsdepending on the speed of change in the data and the usefulness ofcontinuous data streams for any given data set or field.

Velocity—The data is accumulated in real-time and at a rapid pace. Theautomated data consolidation module 100,200 of this disclosure maximizesdata velocity by automatically recording everything which removes manualdata entry that slows the data acquisition process down. The ability toperform real-time analytics against such high-volume data in motioncould revolutionize healthcare.

Variety—How different is the data. The automated data consolidationmodule 100,200 of this disclosure maximizes data variety by recordingmost if not all structured and semi-structured data sources in the OR orother care locations, including but not limited to: physiologicmonitors, equipment, medication injections, dose events, responseevents, fluids and video with artificial intelligence (AI) and/ormachine learning (ML) analysis of the patient and delivered care. Insome examples, the automated data consolidation module 100,200 may alsoaccept unstructured textual data entered by a keyboard, natural languagevoice recognition, from the EMR/EHR or other suitable source.

Veracity—How accurate is the data, the truthfulness of the data. Theautomated data consolidation module 100,200 of this disclosure maximizesdata veracity by automatically recording the data without requiringhuman input. In some examples, the automated data consolidation module100,200 of this disclosure includes Sensor Fusion, a data analyticsystem that monitors input data in order to reject clearly mistakeninputs such as might occur if an EKG lead comes lose forexample—improving the veracity of the database. Sensor Fusion analyzesthe input data from multiple sensors simultaneously in order to detectand reject inputs that are clearly erroneous. In some examples, SensorFusion may include a threshold-controlled artifact-removal module and/ora Kalman filter using statistical analysis to compare the input datastreams.

In some examples, the automated data input feature of the automated dataconsolidation module 100,200 is particularly important because theincreased variety of data input and high velocity of data input hinderthe ability to cleanse data before analyzing it and making decisions,magnifying the issue of data “trust.” The mere fact that the input isautomatic decreases the opportunity for human error.

In some examples as shown in FIGS. 1 and 2, module 101,201 of theautomated data consolidation module 100,200 may consolidate a widevariety of equipment used for anesthesia and surgery. Some of thisequipment, including but not limited to: the physiologic monitors, theurine output monitor and the blood loss monitor and video observation ofthe patient with AI and/or ML analysis to produce response event datathat can be automatically recorded by the automated data consolidationmodule 100,200.

In some examples as shown in FIGS. 1 and 2, module 101,201 of theautomated data consolidation module 100,200 may consolidate a widevariety of equipment that produce dose event data, including but notlimited to: medication identification and measurement system 128,228 andthe IV fluid identification and measurement system 130,230, gas flowmeters (not shown), the mechanical ventilator (not shown), thepneumoperitoneum insufflator (not shown), the electrosurgical generator(not shown) and video observation of the patient and provider with AIand/or ML analysis to produce dose event data that can be automaticallyrecorded by the automated data consolidation module 110,200 (e.g., doseevent data can be received by processing circuitry 157, 257 and storedon one or more storage devices).

The automated data consolidation module 100,200 not only physicallyconsolidates various electrical and electromechanical equipment but alsoconsolidates the data outputs of the various equipment for easyelectrical communication with the processing circuitry 157, 257. Theconsolidation of equipment in and on the module 101,201 of the automateddata consolidation module 100,200 solves another practical obstaclecurrently preventing an automated dose/response record. Currently, muchof the equipment mentioned above does not produce digital data thatdocuments the equipment's operation because the various equipmentmanufacturers have chosen not to provide output data. The practicalsolution is that the provider of the automated data consolidation module100,200 can require that any equipment that is to be located in or onthe module 101,201, must include data outputs and the provider can alsoprescribe the digital standard and format so that the data can beorganized efficiently and completely in a “big data” database. Thesebenefits are not provided in any conventional system.

In some examples, it is anticipated that the automated dataconsolidation module 100,200 can also accept input data from othermedical equipment and data sources by either wired or wirelessconnections. As with the equipment housed within or on the automateddata consolidation module 100,200, it is preferable if the input datafollows the digital standard and format applied to the other equipment.However, in some examples the processing circuitry 157, 257 cantranslate input data that is not in the desired digital standard orformat.

Machine vision cameras and software are very good at measuringdistances, movements, sizes, looking for defects, fluid levels, precisecolors and many other quality measurements of manufactured products.Machine vision cameras and software can also be “taught” through AI andML to analyze complex and rapidly evolving scenes such as those in frontof a car driving down the road. Machine vision cameras and softwaredon't get bored or distracted.

FIG. 12 illustrates an isometric view of another example module 1201including an automated data consolidation module 1200 for generating anautomated electronic anesthetic record located proximate to a patient.

In some examples as shown in FIG. 12, the automated data consolidationmodule 1200 of this disclosure includes systems and methods for usingmachine vision cameras 1299A,B and software to “observe” the patient. Ifthe patient is in surgery, the patient's head may be the focus of theobservation. In some examples, during surgery the machine vision cameras1299A,B and software may be “looking” for dose events including but notlimited to mask ventilation, endotracheal intubation or airwaysuctioning. In other words, the machine vision cameras 1299A,B cangenerate and send dose event information to be received and analyzed byprocessing circuitry 157, 257.

In some examples, during surgery the machine vision cameras 1299A,B andsoftware may be “looking” for (e.g., monitoring, sensing, detecting)response events including but not limited to: movement, grimacing,tearing or coughing or changes in skin color or sweating. In otherwords, the machine vision cameras 1299A,B can generate and send responseevent information to be received and analyzed by processing circuitry157, 257. Movement, grimacing, tearing and coughing are all signs of“light” anesthesia and that the patient may be in pain. Subtle changesin skin color from pinkish to blueish or grayish may indicate inadequateoxygenation or low perfusion. Subtle changes in skin color from pinkishto reddish may indicate hyperthermia or an allergic reaction. Sweatingmay indicate inadequate perfusion or hyperthermia. In any event,observing the patient is regarded by the American Society ofAnesthesiologists as the most basic of all monitors. Machine visioncameras 1299A,B and software do not get bored and can be programmed torecognize subtle changes that may be missed by the human observer. Bothmovements and skin color changes may be subtle. The baseline color maybe very difficult for the observer to remember, whereas the machinevision cameras 1299A,B and software can precisely “remember” thebaseline color and recognize even subtle changes over time. In someexamples, pain may be detected by facial expression analysis, such as byprocessing circuitry 1257 receiving response event information from themachine vision cameras 1299A,B, analyzing such response eventinformation, and recording or displaying the information, or alerting aclinician or other personnel via a user interface, such as the display1226 or the remote display 1287, shown in FIG. 12.

In some examples, vital signs such as heart rate, respiration rate,blood oxygen saturation and temperature can be measured remotely viacamera-based methods. Vital signs can be extracted from the opticaldetection of blood-induced skin color variations—remotephotoplethysmography (rPPG). In some examples, this technique works bestusing visible light and Red Green Blue (RBG) cameras. However, it hasalso been shown to work using near-infrared light (NIR) and NIR cameras.Just like pulse oximetry, a well-known technology for detecting pulseand blood oxygen saturation, rPPG relies on the varying absorption ofdifferent wavelengths of light caused by blood volume changes and theoxygen saturation of the blood in the small blood vessels beneath theskin. Unlike pulse oximetry that needs to be in contact with the skin,rPPG enables contactless monitoring of human cardiac activities bydetecting the pulse-induced subtle color changes on the human skinsurface using a regular RBG camera.

In some examples, this disclosure anticipates automating the remotephotoplethysmography using techniques such as, but not limited to,region of interest (RoI) detection and full video pulse extraction(FVP). In some examples, this disclosure also anticipates using acombination of visible light and near-infrared light wavelengths todetect different parameters. In some examples, additional measurementsare anticipated using the rPPG technology including but not limited tomeasuring blood glucose and hemoglobin levels.

FIG. 13 illustrates an isometric view of yet another example module 1301including an automated data consolidation module 1300 for generating anautomated electronic record located proximate to a patient.

In some examples as shown in FIG. 13, if the patient is on the ward, inthe nursing home or other long-term care facility or at home, the wholepatient may be the focus of the observation. In some examples, if thepatient is on the ward, in the nursing home or other long-term carefacility, or at home, the machine vision cameras 1399 and software maybe “looking” for (e.g., sensing, monitoring, detecting) dose eventsincluding but not limited to repositioning the patient or suctioning theairway, feeding and eating, or assisting the patient out of bed or anyother nursing procedure.

In some examples, if the patient is on the ward, in the nursing home orother long-term care facility, or at home, the machine vision cameras1399 and software may be “looking” (e.g., sensing, monitoring,detecting) for response events including but not limited to restlessnessor getting out of bed without assistance, coughing or changes in thebreathing pattern or changes in skin color. Recent research has shownthat machine vision cameras 1399 and software with AI can recognizemoods with reasonable accuracy. The patient's mood could be an importantresponse event that should be recorded.

As shown in technique 2400 of FIG. 24, various operations can beperformed by the dose-response systems 100, 200, 1200, 1300 andsubsystems disclosed herein. While the technique 2400 can be performedby the dose-response systems 100, 200, 1200, 1300, the technique 2400can also be performed by other systems, and the systems 100, 200, 1200,1300 can perform other techniques. In an example, a dose-response systemcan perform technique 2400 using processing circuitry and one or morestorage devices including a memory. The memory can have instructionsstored thereon, which when executed by the processing circuitry causethe processing circuitry to perform the operations. For example,operation 2402 can include receiving dose information (or dose event)from one or more sensors. Operation 2404 can include receiving responseinformation (or response event) from one or more sensors. The doseinformation can include data corresponding to a dose provided to thepatient, and the response information can include data corresponding toa response of the patient. Operation 2406 can include temporallycorrelating the dose information and the response information. Operation2408 can include saving the temporally correlated dose-responseinformation to at least one of the one or more storage devices such asstorage device 1516 of FIG. 15. Temporally correlating dose-responseinformation can include continuously temporally correlating thedose-response information, such as in a processor-based timer, in abeat-by-beat, second-by-second type manner, as opposed to the extendedintervals and less accurate methods of traditional medical practice. Insome examples, temporally correlating the dose-response informationincludes temporally correlating the dose-response information, such asin a range between every 0.1 seconds to 5 minutes. In some examples, itmay be preferable to temporally correlate in a range between every 0.1seconds to 1 minute. It may be more preferable to temporally correlatein a range between every 1 second to 1 minute, however, any suitabletemporal correlation may be used to facilitate accurate, safe andmeaningful correlation where a direct, time-based relationship betweenthe dose and the response is captured.

In some examples, operation 2410 can include displaying informationcorresponding to the dose-response data. In operation 2412, if theprocessing circuitry detects a concerning response, the processingcircuitry can alert a user to the response. Operation 2414 can includeanalyzing the temporally correlated data. In some examples, operation2414 can include aggregating the temporally correlated dose-responseinformation with temporally correlated dose-response informationcollected from other patients. In some examples, operation 2414 caninclude aggregating the temporally correlated dose-response informationwith temporally correlated dose-response information collected from thesame patient over a different period of time. Comparing data from thesame patient may allow a provider to detect when a patient develops atolerance to a drug, such as a pain killer. In an example, if the rateof grimacing by the patient increases or they start to grimace sooner(e.g., response) after being given a pain medication (dose), as the painmedication is given over different days, weeks and months. Operation2414 can also facilitate machine learning.

In technique 2400, the dose information can include data/eventsgenerated by one or more of: an RFID interrogator, a barcode reader, aQR reader, a machine vision digital camera, any combination thereof, orany other suitable sensor. The response information can includedata/events generated by a machine digital camera or any other suitablesensor. The response information can include an image of one or moremovements, secretions or skin color changes, and the processingcircuitry can be configured to identify changes in the responseinformation. In some examples the processing circuitry can use machinelearning to identify changes in the response information. In someexamples, the response information includes physiologic data generatedby a physiologic monitor. For example, the physiologic data can includeat least one of: electrocardiogram, pulse oximetry, blood pressure,temperature, end-tidal CO₂, expired gases, respiratory rate, hemoglobin,hematocrit, cardiac output, central venous pressure, pulmonary arterypressure, brain activity monitor, sedation monitor, urine output, bloodloss, blood electrolytes, blood glucose, blood coagulability,train-of-four relaxation monitor data, IV extravasation monitor data andbody weight, and any combination thereof or other suitable physiologicdata.

Implementation of any of the techniques described herein may be done invarious ways. For example, these techniques may be implemented inhardware, software, or a combination thereof. For a hardwareimplementation, the processing units may be implemented within on ormore application specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,other electronic units designed to perform the functions describedabove, and/or a combination thereof.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine-readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the techniques may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing thetechniques described herein. For example, software codes may be storedin a memory. Memory may be implemented within the processor or externalto the processor. As used herein the term “memory” refers to any type oflong term, short term, volatile, nonvolatile, or other storage mediumand is not to be limited to any particular type of memory or number ofmemories, or type of media upon which memory is stored.

There are two common modes for ML: supervised ML and unsupervised ML.Supervised ML uses prior knowledge (e.g., examples that correlate inputsto outputs or outcomes) to learn the relationships between the inputsand the outputs. The goal of supervised ML is to learn a function that,given some training data, best approximates the relationship between thetraining inputs and outputs so that the ML model can implement the samerelationships when given inputs to generate the corresponding outputs.Unsupervised ML is the training of an ML algorithm using informationthat is neither classified nor labeled, and allowing the algorithm toact on that information without guidance. Unsupervised ML is useful inexploratory analysis because it can automatically identify structure indata. Either of supervised ML or unsupervised ML can be used to trainthe systems described herein to correlate information from one or moresensors, such as a machine vision digital camera as having a particularmeaning. For example, either of supervised or unsupervised ML can beused to train the systems to correlate facial expressions with responseevents. The system can be trained to “read” a particular patient, or agroup of patients that may or may not include the particular patientbeing monitored.

Common tasks for supervised ML are classification problems andregression problems. Classification problems, also referred to ascategorization problems, aim at classifying items into one of severalcategory values (for example, is this object an apple or an orange?).Regression algorithms aim at quantifying some items (for example, byproviding a score to the value of some input). Some examples of commonlyused supervised-ML algorithms are Logistic Regression (LR), Naive-Bayes,Random Forest (RF), neural networks (NN), deep neural networks (DNN),matrix factorization, and Support Vector Machines (SVM).

Some common tasks for unsupervised ML include clustering, representationlearning, and density estimation. Some examples of commonly usedunsupervised-ML algorithms are K-means clustering, principal componentanalysis, and autoencoders.

FIG. 15 illustrates an example electronic and/or electromechanicalsystem 1500 of a medical system in accordance with some examplesdescribed herein. The system 1500 will be described with respect to theautomated data consolidation module 100, 200, but can include any of thefeatures described herein to perform any of the methods or techniquesdescribed herein, for example, by using the processor 1502. Theprocessor can include processing circuitry 157 or 257 of FIGS. 1 and 2.In some examples, the processing circuitry 1502 can include but is notlimited to, electronic circuits, a control module processing circuitryand/or a processor. The processing circuitry may be in communicationwith one or more memory and one or more storage devices. A singleprocessor can coordinate and control multiple, or even all the aspectsof the system 1500 (e.g., of modules 101, 201), or multiple processorscan control all the aspects of the system 1500. In some examples thestorage device 1516 or memory 1504, 1506, 1516 can include at least aportion of the patient's anesthetic record saved thereon. The system1500 can also include any of the circuitry and electronic and/orelectromechanical components described herein, including but not limitedto, any of the sensor(s) described herein (e.g., sensors 1521), such asbut not limited to, RFID barcode or QR codes sensors, machine visioncameras, retinal scanners, facial recognition scanners, fingerprintreaders, actuators and position sensors described herein. The system1500 may also include or interface with accessories or other featuressuch as any of: a remote display or wireless tablet (e.g., 287, FIG. 2),as well as any of the other systems described herein.

The processing circuitry 1502 can receive information from the varioussensors described herein, make various determinations based on theinformation from the sensors, output the information or determinationsfrom the information for output on the display or wireless tablet,output instructions to provide an alert or an alarm, power variouscomponents, actuate actuators such as clamps and flow managing devices,etc., or alert another system or user, as described herein. For the sakeof brevity, select systems and combinations are described in furtherdetail above and in the example sets provided in the Notes and VariousExamples section below. Other embodiments are possible and within thescope of this disclosure.

Further, FIG. 15 illustrates generally an example of a block diagram ofa machine (e.g., of module 101, 201) upon which any one or more of thetechniques (e.g., methodologies) discussed herein may be performed inaccordance with some embodiments. In alternative embodiments, themachine 1500 may operate as a standalone device or may be connected(e.g., networked) to other machines. In a networked deployment, themachine 1500 may operate in the capacity of a server machine, a clientmachine, or both in server-client network environments. The machine1500, or portions thereof may include a personal computer (PC), a tabletPC, a personal digital assistant (PDA), a mobile telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein, such as cloud computing, software as aservice (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or like mechanisms. Such mechanisms aretangible entities (e.g., hardware) capable of performing specifiedoperations when operating. In an example, the hardware may bespecifically configured to carry out a specific operation (e.g.,hardwired). In an example, the hardware may include configurableexecution units (e.g., transistors, circuits, etc.) and a computerreadable medium containing instructions, where the instructionsconfigure the execution units to carry out a specific operation when inoperation. The configuring may occur under the direction of theexecution units or a loading mechanism. Accordingly, the execution unitsare communicatively coupled to the computer readable medium when thedevice is operating. For example, under operation, the execution unitsmay be configured by a first set of instructions to implement a firstset of features at one point in time and reconfigured by a second set ofinstructions to implement a second set of features.

Machine (e.g., computer system) 1500 may include a hardware processor1502 (e.g., processing circuitry 157, 257, a central processing unit(CPU), a graphics processing unit (GPU), a hardware processor core, orany combination thereof), a main memory 1504 and a static memory 1506,some or all of which may communicate with each other via an interlink(e.g., bus) 1508. The machine 1500 may further include a display unit1510, an alphanumeric input device 1512 (e.g., a keyboard), and a userinterface (UI) navigation device 1514 (e.g., a mouse). In an example,the display device 1510, an input device such as an alphanumeric inputdevice 1512 and UI navigation device 1514 may be a touch screen display.The display unit 1510 may include goggles, glasses, or other AR or VRdisplay components. For example, the display unit may be worn on a headof a user and may provide a heads-up-display to the user. Thealphanumeric input device 1512 may include a virtual keyboard (e.g., akeyboard displayed virtually in a VR or AR setting.

The machine 1500 may additionally include a storage device (e.g., driveunit) 1516, a signal generation device 1518 (e.g., a speaker), a networkinterface device 1520, and one or more sensors 1521, such as a globalpositioning system (GPS) sensor, compass, accelerometer, or othersensor. The machine 1500 may include an output controller 1528, such asa serial (e.g., universal serial bus (USB), parallel, or other wired orwireless (e.g., infrared (IR), near field communication (NFC), etc.)connection to communicate or control one or more peripheral devices oractuators of the system. Peripheral devices can include but are notlimited to any displays, controllers or memories in electricalcommunication with the system, and actuators can include but are notlimited to: one or more stop-flow clamps 1060A,B (FIG. 10) and one ormore flow rate clamps 1478 (FIG. 14) of the system.

The storage device 1516 may include a machine readable medium 1522 thatis non-transitory on which is stored one or more sets of data structuresor instructions 1524 (e.g., software) embodying or utilized by any oneor more of the techniques or functions described herein. Theinstructions 1524 may also reside, completely or at least partially,within the main memory 1504, within static memory 1506, or within thehardware processor 1502 during execution thereof by the machine 1500. Inan example, one or any combination of the hardware processor 1502, themain memory 1504, the static memory 1506, or the storage device 1516 mayconstitute machine readable media that may be non-transitory.

While the machine readable medium 1522 is illustrated as a singlemedium, the term “machine readable medium” may include a single mediumor multiple media (e.g., a centralized or distributed database, orassociated caches and servers) configured to store the one or moreinstructions 1524.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 1500 and that cause the machine 1500 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine-readable medium examples mayinclude solid-state memories, and optical and magnetic media. Specificexamples of machine readable media may include: non-volatile memory,such as semiconductor memory devices (e.g., Electrically ProgrammableRead-Only Memory (EPROM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM)) and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

The instructions 1524 may further be transmitted or received over acommunications network 1526 using a transmission medium via the networkinterface device 1520 utilizing any one of a number of transferprotocols (e.g., frame relay, internet protocol (IP), transmissioncontrol protocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 1520 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 1526. In an example, the network interfacedevice 1520 may include a plurality of antennas to wirelesslycommunicate using at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 1500, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

Method examples described herein may be machine or computer-implementedat least in part. Some examples may include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theexamples. An implementation of such methods may include code, such asmicrocode, assembly language code, a higher-level language code, or thelike. Such code may include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code may be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

Some of the benefits of the automated data consolidation module 100, 200of FIGS. 1 and 2 and the subsystems described throughout thisdisclosure, and including the machine 1500 (FIG. 15), can includefeatures to help with monitoring medication, fluid and anesthesiadelivery, as well as documenting medication, fluid and anesthesiadelivery, as well as other functions. In general, doctors and nurses arenot interested in replacing themselves and their jobs with automateddrug delivery or automated anesthesia systems. However, there is greatinterest in automated record keeping. Virtually all healthcare providerswould prefer the “old” paper record and a pen to the “new” computerrecords. Filling out the electronic medical record (EMR) using acomputer keyboard, mouse and various menus is widely viewed as a slow,cumbersome and distracting process. The challenge with automated recordkeeping is automating the data input that documents the numerousactivities, anesthesia related events, fluid, gas and medicationadministration, ventilator settings, pressure off-loading effectiveness,as well as outputs such as blood loss and urine output, that constitutean anesthetic experience.

A challenge in implementing the automated data consolidation module 100,200 with an automated electronic anesthetic record (EAR) or electronicmedical record (EMR) is to force as little change in the caregiver'sroutine as possible onto the clinicians using this system. Medicalpersonnel tend to be creatures of habit and tradition and they generallydo not like change. For example, IV medications are traditionallyadministered from a syringe and the dose is determined by the caregiverobserving the plunger moving relative to a scale printed on the syringe.Maintaining this general technique of drug administration may have thehighest probability of acceptance by healthcare users who are typicallyslow to embrace changes in their routine.

Further, with regard to benefits of the modules, systems and machinesdescribed herein, the automated data consolidation module 200 of module201 can generate an automated electronic medical record (EMR) with themodule 201 locatable proximate to the patient 202. The module 201 can bea module for housing unrelated electronic and electromechanical surgicalequipment. The module 201 need not necessarily be configured to houseunrelated electronic and electromechanical surgical equipment in allexamples, and other modules can include the system for generating anautomated EMR.

The module 201 can be an automated EMR system that can include one ormore systems (e.g., 200, 228, 230) configured to measure (e.g., monitor)and record one or more of functions involved in a surgical anestheticenvironment, and can include life support functions. The one or moresystems 228, 230 can measure and record data automatically. However, insome examples, a user may initiate any of the systems described hereinto measure and/or record data. These various measurements may beelectronically recorded (such as on mass storage 1516 (FIG. 15) anddisplayed on the electronic anesthetic record display 226 (e.g., displaydevice 1510, FIG. 15). Inputs to the automated EMR system may be managedby the anesthetic record input component 224 (e.g., input device 1512;FIG. 15). The anesthetic record input component 224 (e.g., input device1512; FIG. 15) can include a touch-screen display 226 that organizes allof the inputs to the EMR into easily accessed and utilized information.In some examples, and as shown in FIG. 2, the identification andmeasurement system 228 of this disclosure may be located proximate thepatient 202. The control displays for the identification and measurementsystem 228 may include a dedicated display proximate the identificationand measurement system 228 or may be shared space on the anestheticrecord input component 224 or display 226. In these locations, theinformation and controls of the identification and measurement system228 can be viewed by the anesthesiologist or other user, in a singlefield of vision with the patient and surgical field.

Example methods of employing the systems, modules and machines disclosedherein are described throughout this disclosure and in the methods ofFIGS. 16-21 which are illustrative in nature. Other methods describedherein may also be performed by the systems, modules and machinesdescribed herein, and the systems modules and machines described hereinmay be used to perform other methods.

FIGS. 16-18 show flow charts illustrating techniques for identification,measurement, monitoring, security and safety related to medicationsand/or IV fluids. The methods may be used with the systems, sub-systemsand modules of FIGS. 1-15 (e.g., 101, 201, 1500), but may also be usedwith other systems. Likewise, the systems, subsystems of modules ofFIGS. 1-15 may also be used with other methods. The techniques 1600,1700, 1800, 1900, 2000, 2100, 2300 can be performed by at least onenon-transitory machine-readable medium (e.g., computer readable)including instructions for operation of the systems described herein.Some steps of techniques may be performed by a provider. The systems caninclude processing circuitry (e.g., 157, 257, 1500, including one ormore processors, processing circuitry hardware) for executing theinstructions. The instructions, when executed by the processingcircuitry can cause the processing circuitry to perform operationsdescribed in FIGS. 16-21 and 23, and as described in the examplesthroughout this disclosure.

FIG. 16 is a flow chart illustrating an example technique 1600 of IVfluid identification and measurement. To start the technique, inoperation 1602 a provider hangs an IV fluid bag and attached dripchamber on electronic scale hooks in an IV fluid identification andmeasurement unit (e.g., FIG. 14). In operation 1604, a machine visioncamera and software can identify the fluid and bag by the barcode labelon the IV fluid bag. In operation 1606, the machine vision camera andsoftware can identify the individual drops in the drip chamber andmeasure the size of the drop to determine the fluid volume per drop andcount the number of drops per unit time. In operation 1608 the machinevision camera and software can calculate the flow rate by multiplyingthe number of drops per unit time by the volume/drop. In operation 1610,the fluid flow rate is displayed and document in the EMR.

In operation 1612, if the machine vision camera and software fails toidentify individual drops in the drip chamber, in operation 1614 themachine vision camera and software can look for a floating ball (e.g.,float) that is located in the drip chamber to determine if the ball isfloating and if the ball is moving. In operation 1616, when the ball isnot floating and/or moving, IV clamps are closed and the provider canchange the empty IV bag if necessary. In operation 1618, if the machinevision camera and software can determine that the ball is floating andmoving, the system determines that the fluid flow is so fast that thefluid flow is constant or continuous such that individual drops cannotbe measured. In operation 1618, because individual drops cannot bedetermined, the system switches to measuring the fluid flow rate usingan electronic IV scale (FIG. 14) to determine the fluid flow rate. Inoperation 1620, the fluid flow rate can be determined by monitoring thechange in IV bag weight per time. In operation 1622, the fluid flow ratecan be displayed and documented in the EMR.

FIG. 17 is a second flow chart illustrating a technique 1700 includingaspects of the technique 1600 of IV fluid identification and measurementfrom the perspective of processing circuitry (e.g., 257, FIG. 2; 1502,FIG. 15). The technique 1700 may include an operation 1702 to receive IVfluid identification information from a first IV sensor (e.g., one ormore sensors), such as an RFID or barcode reader to identify the fluidor other characteristics of an IV fluid bag as described herein.Operation 1704 can include saving the IV identification information to astorage device (e.g., one or more storage devices, memory, EMR).Operation 1706 can include to receive fluid drop information from asecond IV sensor, such as a machine vision camera that detects, sensesand measures an individual drop in a drip chamber to determine a fluidvolume per drop and measure the number of drops per unit of time. Whilethe illustrative example of FIG. 17 includes the first IV sensor and thesecond IV sensor, in some examples the first IV sensor and the second IVsensor can be the same sensor or same one or more sensors. Operation1708 can include to determine if a fluid drop was recognized by thesecond IV sensor. If in operation 1708 it is determined that a fluiddrop was recognized, operation 1710 can include determining a fluid flowrate, such as by calculating the flow rate by multiplying the number ofdrops per unit time by the volume per drop. In some examples, the volumeper drop is measured, while in other examples the volume per drop may beinput by a user, or can be a value retrieved from a memory. Operation1712 can include transmitting instructions to a display to cause a fluidflow rate to be displayed. Operation 1714 can include saving flow rateinformation to the storage device to document the fluid flow rate in theEMR. Any time a change is input or detected in the system, updated flowrate information can be displayed and saved.

If in operation 1708 it is determined that a fluid drop was notrecognized, operation 1616 can include receiving float information fromthe second IV sensor or another IV sensor. The float information caninclude information about a float in the drip chamber including is thefloat still (e.g., not moving), moving, or is movement of the floatslowing down. Operation 1718 can include determining if the float ismoving. If the float is moving, Operation 1720 can include determiningthe fluid flow is constant. In such a scenario, the fluid is flowing butthe fluid is flowing so quickly that individual drops of fluid cannot bedistinguished because the fluid is flowing as a steady stream. Operation1720 can further include determining the fluid flow rate by receiving IVbag physical characteristic information from a physical characteristicsensor, such as a weight sensor. The physical characteristic informationcan include weight information from the weight sensor (e.g., scale).Operation 1722 can include determining the fluid flow rate bycalculating the change in IV bag weight over a period of time. In otherexamples, instead of weight information, the physical characteristicinformation can include a position of the IV bag that changes as aresult of a change in weight, without the physical characteristic datacorresponding directly to a weight measurement. Other physicalcharacteristics and other physical characteristic sensors configured tomonitor IV fluid delivery may be provided such that an automated, or atleast partially automated EMR system is enabled.

If in operation 1718 it is determined that the float is not moving,operation 1728 can include determining that no fluid is flowing from theIV bag and transmitting one or more of: an instruction an actuator suchas a clamp, to cause the actuator to inhibit fluid flow to the patient(e.g., close the clamp onto IV tubing to prevent flow); and transmit andinstruction to an indicator (e.g., display, audible, tactile indicator)to cause an alert to be generated. Operation 1730 can include saving ano fluid event to the storage device.

FIG. 18 is a flow chart illustrating an example technique 1800 ofmedication identification and measurement. In operation 1802 a providerinserts a medication syringe into an injection portal (e.g., 411, FIG.4). In operation 1804 the medication can be identified by a sensor suchas by at least one of the RFID, barcode or QR sensors described herein.In operation 1806 processing circuitry checks for medication errors bycomparing the medication against one or more of: a doctor's orders,allergy history, medical history, other medications and current vitalsigns. In operation 1808, the results of the medication error check canbe displayed on an electronic record display. The results can indicateno error, the presence of an error, specific details about the error, orpresent a link to access information including additional details aboutthe error. In operation 1810, if a serious medication error isrecognized, the error deploys (e.g., causes actuation of) IV tubingclamps (e.g., 1060A, 1060B of FIG. 10) to prevent injection of themedication.

If in operation 1812, such as when no errors are determined, the machinevision camera and software can measure the diameter of the syringe. Inoperation 1814, an image of, or representation of the image of thesyringe, is displayed on the electronic record display. In operation1816 the provider squeezes the plunger of the syringe. In operation1818, the machine vision camera and software measure the distancetraveled by the syringe's plunger seal (e.g., 548, FIG. 5). In operation1820 the processing circuitry calculates the volume injected bymultiplying the syringe diameter times the distance of plunger travel.The processing circuitry can also calculate the dose by multiplying thevolume injected by the concentration of the medication. In operation1822 the injected dose and volume are displayed on the electronic recorddisplay. In operation 1824 the injected dose and volume are time stampedand recorded in the electronic medical record.

FIG. 19 is a second flow chart illustrating a technique 1900 includingaspects of the technique 1800 of medication identification andmeasurement from the perspective of processing circuitry (e.g., 157,FIG. 1; 257, FIG. 2; 1502, FIG. 15).

Technique 1900 can include an operation 1902 to receive medicationidentification information such as medication type, concentration,brand, lot number or amount, from a first medication sensor (e.g., RFID,barcode or QR reader). Operation 1904 can include saving medicationidentification information to a storage device (e.g., one or morestorage devices, memory). Operation 1906 can include comparingmedication identification information to at least one of a medicationorder, allergy history, medical history, other medications ordered forthe patient, and vital signs (e.g., previously obtained vital signs orcurrent vital signs of the patient via continuous monitoring). Operation1908 can include determining if a medication error is present. Operation1910 can include receiving syringe information from a second medicationsensor (e.g., a sensor configured to measure diameter, such as a machinevision camera). Operation 1912A can include receiving medicationdelivery information from the second medication sensor or anothermedication sensor. In some examples, the medication delivery informationcan include a distance of a syringe plunger travel.

Operation 1912B can include transmitting instructions to a display tocause an image of the syringe (e.g., actual image or representation ofthe syringe) to be displayed. A representation of the syringe caninclude an image communicating information about the syringe that is notan image of the actual syringe or can be a modified image of thesyringe, such as to highlight or point out aspects of the syringe ormedication within the syringe

Using the medication delivery information obtained in operation 1912A,operation 1914 can include determining a medication delivery amount.Operation 1916 can include transmitting instructions to a display (e.g.,display 226, FIG. 2) to cause the medication delivery information ormedication delivery amount to be displayed.

If in operation 1908 it is determined that a medication error ispresent, operation 1920 can include transmitting instructions includingerror information to the display or another display to cause the errorinformation to be displayed. In some examples, any of the instructionsdescribed herein that are sent to the display can be sent to one or moredisplays. Such displays can be located locally or remotely (e.g., in adifferent part of a room, in a separate room, in another building, inanother state, in another country), to alert multiple providers. Forexample, a provider such as a nurse anesthetist located adjacent to thepatient can be alerted to and provided with the information via display226. In addition, a second provider, such as an anesthesiologistsupporting the nurse anesthetist, and who may be supporting other nurseanesthetists working in different rooms, can also be alerted on adisplay of a mobile device, which may prompt them to check in with andpotentially assist the nurse anesthetist. This concept can be appliedoutside the operating room to manage medication delivered by providersworking in different rooms of a hospital or other care center, while asecond provider such as a nurse manager, nurse practitioner, pharmacistor doctor oversees the work of the first provider. In operation 1922 theerror information can be saved to one or more storage devices (e.g.,259, FIG. 2; 1516, FIG. 15).

Also in response to determining that a medication error has occurred inoperation 1908, operation 1924 can include transmitting instructions toan actuator such as an IV tubing clamp to inhibit (e.g., prevent,reduce, limit) injection. In some examples, the actuator can reduce orlimit the amount of the injection to a specified amount rather thancompletely inhibiting or preventing administration of the medication.Operation 1926 can include saving an inhibit injection event informationto a storage device, such as any of the one or more storage devices(e.g., 259, FIG. 2; 1516, FIG. 15). The inhibit injection eventinformation can include information such as the time of the event andthe action taken to inhibit injection and how much the injection wasinhibited (e.g., partially inhibited, completely inhibited, or amount ofmedication inhibited from injection).

If in operation 1908 it is determined that a medication error has notoccurred, operation 1910 can include receiving syringe informationincluding a syringe diameter from a sensor such as a machine visioncamera. In some examples, the sensor can be the first medication sensoror can be a second medication sensor. Operation 1912A can includereceiving medication delivery information from a sensor such as from thefirst medication sensor, the second medication sensor or another sensor.The medication delivery information can include a distance of plungertravel relative to a syringe body. Operation 1912B can includetransmitting instructions to one or more displays such as, display 226,FIG. 2, to cause an image of the syringe or representation of thesyringe to be displayed.

Operation 1914 can include determining a medication delivery amount,such as a volume or dose injected. For example, the volume injected canbe calculated by multiplying the syringe diameter by the distance ofplunger travel. The dose injected can be calculated by multiplying thevolume injected by the concentration of the medication.

Operation 1916 can include transmitting instructions to the one or moredisplays to cause the medication delivery information to be displayed.Operation 1918 can include saving medication delivery information to thestorage device (e.g., EMR). In some examples, the medication deliveryinformation can include, but is not limited to, volume, dose, time ofthe injection, or time period of the injection.

FIG. 20 is a flow chart illustrating an example of a second technique ofIV fluid identification and measurement including safety and securitymeasures. Aspects of technique 2000 can be similar or the same astechniques 1800 and 1900, however, technique 2000 is particularlywell-suited to the challenges of maintaining safety and security withcontrolled drugs such as narcotics. Technique 2000 can include operation2002 of identifying a medication (e.g., a controlled drug) andidentifying a health care provider, such as by RFID, barcode or QR codereader, retinal scanner, facial recognition, or fingerprint. Operation2002 can occur at the time a provider checks out a drug from a pharmacyor a medication dispensing machine. The medication can include anarcotic in a tamper-proof, non-refillable syringe.

Operation 2004 can include identifying a provider such as by RFID,barcode or QR code reader, retinal scanner, facial recognition, orfingerprint at a patient's bedside, such as at an injection portal(e.g., 411, FIG. 4). The provider can be the same or a differentprovider as the provider in operation 2002. In operation 2006, theprovider inserts the medication syringe into the patient's injectionportal. In operation 2008, the controlled drug is identified, such as byan RFID, barcode or QR code reader associated with the injection portal.Operation 2010 can include processing circuitry checking for medicationerrors by comparing the medication against doctor's orders, allergyhistory, medical history, other medications and vital signs. Operation2012 can include displaying medication error check results on a display,such as display 226, FIG. 2. If the medication error is of a seriousnature, the error can cause IV tubing clamps to prevent injection.Operation 2016 can include machine vision camera and software measuringthe diameter of the syringe. Operation 2018 can include an image, or animage representing the syringe being displayed on a display, such asdisplay 226, FIG. 2. Operation 2020 can include a provider squeezing theplunger of the syringe. Operation 2022 can include the machine visioncamera and software measuring the distance traveled by the syringe'splunger seal (e.g., 548, FIG. 5). Operation 2024 can include processingcircuitry determining the volume of medication injected by multiplyingthe syringe diameter by the distance of plunger travel or determiningthe dose of medication injected by multiplying the volume of medicationinjected by the concentration of the medication. Operation 2026 caninclude displaying the injected volume or dose on a display, such asdisplay 226, FIG. 2.

Operation 2028 can include saving the injected volume or dose along witha timestamp to the EMR. Operation 2030 includes repeating the operationsof technique 2000 as necessary until the machine vision camera andsoftware documents an empty syringe. Operation 2032 includes completingthe “chain of custody” for a specific syringe of controlled medication.The operations of technique 2000 can be repeated as necessary for othersyringes, thereby completing the “chain of custody” for each syringe.

FIG. 21 is a second flow chart illustrating a technique 2100 includingaspects of the technique 2000 of IV fluid identification and measurementincluding safety and security measures from the perspective ofprocessing circuitry, such as, but not limited to, processing circuitry157, FIG. 1; 257, FIG. 2; 1502, FIG. 15. The technique may involveprocessing circuitry 2202B, such as may be part of a medication vendingsystem as shown in FIG. 22. FIG. 22 illustrates generally an example ofa block diagram of vending system and a medication delivery system ofFIGS. 1-21 and 23 upon which any one or more of the techniques (e.g.,methodologies) discussed herein may perform in accordance with someembodiments. FIGS. 21 and 22 are described together.

In some examples, operations 2102 and 2104 can be part of a vendingsystem (2202, FIG. 22) for managing medication withdrawal from apharmacy or other vending system. Operations 2110-2136 can be part of amedication delivery system (e.g., can be used with the bedside patientsystems and modules shown and describe in FIGS. 1-15; medicationdelivery system 2210, FIG. 22). Operation 2138 can tie information,including data generated by the vending system 2202 and the medicationdelivery system 2210 together to facilitate tracking a “chain ofcustody” for a specific syringe of controlled medication from thepharmacy until the medication is completely injected into the patient.Chain of custody information can be stored to one or more of: thevending system storage 2202A, the medication delivery storage 2216, andchain of custody storage device (e.g., 2206, FIG. 22) and the EMR. Anyof the storage described herein can include one or more storage devicesor memory as described herein and can include other storage devices inelectrical communication with the vending system or the medicationdelivery system.

Operation 2102 of the vending system can include receiving withdrawingprovider identification information from a medication dispensing sensor2202C, such as a first RFID or barcode reader that reads a badge of aprovider and reads the medication identification information from asyringe or other medication container, or any other type of suitablesensor described herein. Operation 2104 can include associating andsaving the medication identification information and the withdrawingprovider identification information to a vending system storage device(e.g., 2202A, FIG. 22).

Operation 2110 can include receiving medication identificationinformation from a first identification sensor (e.g., RFID, QR, barcodereader, or machine vision camera reads information about a medication)and receiving delivery provider identification information from thefirst identification sensor or another identification sensor (e.g., asecond identification sensor, another RFID, QR or barcode reader,machine vision camera, retinal scanner, facial recognition sensor orfingerprint reader). In some examples, patient identificationinformation can also be obtained from one of the first identificationsensor, second identification sensor or another identification sensor,such as by scanning patient identification information on a hospitalwristband. In some examples receiving the medication identification, theprovider identification information or the patient identification cancause the processing circuitry to send an instruction to a display toprompt the user for the other of the medication identificationinformation, the provider identification information or the patientidentification information.

Operation 2112 can include comparing the received identificationinformation to one or more of: a medication order, allergy history,medical history, other medications and current vital signs. Operation2114 can include determining if a medication error is present. If it isdetermined that a medication error is present, operation 2116 caninclude transmitting instructions including error information to adisplay to cause the error information to be displayed. Operation 2118can include saving the error information to one or more storage devices.Further, if in operation 2114 it is determined that a medication errorhas occurred, operation 2120 can include transmitting inhibit injectioninstructions to an actuator such as , but not limited to, an IV tubingclamp (e.g., 1060A, 1060B; FIG. 10) to inhibit injection. Operation 2122can include saving an inhibit injection event information to one or morestorage devices.

Operation 2124 can include receiving syringe information from a secondmedication sensor (e.g., syringe diameter including syringe innerdiameter, outer diameter, or wall thickness from a machine visioncamera). Operation 2124 can include receiving syringe size informationfrom a data storage device, the syringe size information provided by thesyringe manufacturer that supplies the specific syringes used by thespecific healthcare facility. Operation 2126 can include transmittinginstructions to a display to cause an image of the syringe or arepresentation of the syringe to be displayed. Operation 2128 caninclude receiving syringe movement information from the secondmedication sensor or another sensor. Syringe movement information caninclude, for example, a distance of travel of the syringe plungerrelative to the syringe barrel.

Operation 2130 can include determining medication delivery informationbased on the syringe movement information. Medication deliveryinformation can include, for example, a volume or dose of medicationdelivered to the patient (e.g., ejected from the syringe). In someexamples, the volume of medication delivered (e.g., ejected from thesyringe) can be calculated by multiplying the syringe inner diameter bythe distance of plunger travel. Likewise, the dose of medicationdelivered can be calculated by multiplying the calculated volume by aconcentration of the medication. Operation 2132 can include savingmedication delivery information to one or more storage devices. In otherwords, operation 2132 can include documenting volume, dose and time inan EMR, in some cases automatically without intervention from aprovider.

Operation 2134 can include transmitting instructions to one or moredisplays described herein to cause the medication delivery informationto be displayed. Operation 2136 can include determining that a syringeis empty and saving “chain of custody” complete for the specific syringeof medication (e.g., controlled drug) to one or more storage devices.

To complete and document the chain of custody, thereby ensuring themedication was delivered to the patient, operation 2138 can include oneor more of receiving, associating and saving to one or more chain ofcustody storage devices (e.g., 2206, FIG. 22), information from both thepharmacy vending system 2202 (FIG. 22) and the bedside medicationdelivery system 2210 (FIG. 22) (e.g., 1516; FIG. 15). In some examplesthe one or more chain of custody storage devices is not necessarilyseparate from the vending system storage 2202A or the medicationdelivery storage 2216, but rather can reside with one or the othersystems, a different system, multiple systems or can be included as asingle storage device.

FIG. 23 illustrates and example technique 2300 for assessingphysiological events. In some examples, the EMRs created by theautomated data consolidation module 100, 200, 400, 600, 800 can providethe most accurate and temporally correlated information about therelationship between any injected medication and the resultingphysiologic response. In some examples, this is uniquely accuratedose-response data can be used as a final check of the chain of custodyfor controlled medications or any medication. The processing circuitry157, 257 may include or be in electrical communication with artificialintelligence (AI) and/or machine learning that can compare the measuredphysiologic response in the several minutes after a medication isinjected, to the expected physiologic response for that dose of thatmedication. For example, if the injected medication was a narcotic, itwould be expected that the heart rate and blood pressure of the patientwould decrease quickly after the injection.

In some examples, if the expected physiologic response does not occur,the AI software of the automated data consolidation module 100, 200,400, 600, 800 may electronically “flag” that injection as suspicious.For example, if there is no physiologic response after injecting whatwas supposed to be a narcotic, it is possible that the drug had beenstolen and replaced by saline. On the other hand, no response may simplymean that the patient is addicted to and tolerant of narcotics and thattoo is worth noting. An unpredicted response does not prove anything butmultiple unpredicted responses in multiple patients can be suspicious.Therefore, aggregating or analyzing data over time for a particularpatient or provider can alert management to issues. If any individualprovider traverses a threshold number of flags (e.g., too many “flags”)for unexpected physiologic responses (including no response), theautomated data consolidation module 100, 200, 400, 600, 800 can generatean alert to notify management and an investigation of that provider maybe warranted. Knowing that AI is “watching” the patients' response to ahealthcare providers' injected medications, can be a significantdeterrent to tempted drug thieves.

Technique 2300 can include determining if one or more unexpectedphysiological events has occurred, analyzing saving, aggregating anddisplaying such information, in any order. The method can be performedby processing circuitry 157, 257, 1502, including other processingcircuitry, memories and databases in electrical communication withprocessing circuitry 157, 257, 1502 to one or more of: receivephysiologic data, analyze physiologic data, determine physiologic datais unexpected, create and send instructions to cause an alert to theprovider or another user, or save a physiological event information to astorage device 1516 which may include a database. The physiologicalevent information can include, but not limited to data generated by thevarious sensors and equipment described herein, including one more of:physiological information, patient information, provider information,medication information, time information, location information, facilityinformation, equipment information, aggregated physiological eventinformation and analyzed physiologic event information.

Operation 2302 can include receiving physiologic data from a physiologicsensor, Operation 2304 can include analyzing the physiologic data.Operation 2304 can include comparing the physiologic data to expectedphysiologic responses. Based on the outcome of the analysis in operation2304, in operation 2306, the processing circuitry can determine if thephysiologic data is unexpected, and if so, operation 2308 can includesaving unexpected physiologic event information to one or more storagedevices, or can include sending instructions to one or more displays todisplay unexpected physiologic event information.

Operation 2310 can include aggregating or analyzing physiologic eventinformation from a plurality of unexpected physiological events andgenerating aggregated or analyzed physiologic event information. In someexamples, aggregating can include aggregating a number of physiologicalevents by counting the number of physiological events for a givenprovider, patient, group of patients, medical facility, type ofmedication, or any other suitable assessment. Operation 2312 can includesaving aggregated or analyzed physiologic event information to one ormore storage devices or sending instructions to one or more displays todisplay aggregated or analyzed physiologic event information. In someexamples, the physiologic event information can include any type ofphysiologic event that occurs, including expected or desirablephysiologic events

The operations of technique 2300 can help provide safer care forpatients, including providing narcotic medications when helpful, whilekeeping a close eye on drug abuse by providers or patients. Taken at ahigh level, technique 2300 can help medical facilities evaluate whichmedications are most often abused by patients or stolen by providers,and to mitigate risk for insurers.

Any operations of the various methods described herein can be used incombination with or separately from one another, depending on thedesired features and in consideration of constraints such as financial,space, material and manufacturing availability.

In some examples, a typical patient, for whom a particular physiologicalmeasurement is desired, may be monitored with more than one sensor fromwhich the physiological measurement can be derived. The presentinvention provides a technique for combining the observations from themultiple sensors in order to obtain a more accurate measurement thanwould be available from an individual sensor. The process of combiningthe observations from sensors is defined herein as “sensor fusion.”Although the specific examples presented herein are directed to heartrate estimation, those skilled in the art will readily recognize thatthe principles of the present invention are applicable to otherphysiologic parameters as well. For example: respiratory, oximetry,blood pressure, brain activity, patient movement and depth of anesthesiameasurements can all be provided by a variety of different sensors.Sensor fusion of the present invention can be applied to the multiplesensors to generate a fused respiratory, oximetry, blood pressure, brainactivity, patient movement and depth of anesthesia measurements.

In some examples, the essence of sensor fusion is to utilize redundantmeasurements from multiple sensors to find the best estimate of heartrate at any point in time. Sensor fusion has been used in other areas oftechnology, most notably robotics and radar tracking. However, sensorfusion applications in robotics and radar tracking rely upon a largenumber of sensors and the best estimate is derived by looking for thelargest number of sensors that agree within a defined range ofacceptable variation, a so-called consensus method of fusion.

In its simplest form, sensor fusion can be implemented as an arithmeticaverage of all available sensor measurements of a given parameter suchas heart rate, to find the fused estimate. This approach is not robusthowever, since any error in the individual measurements is included inthe final estimate. As a result, the final estimate may be so differentfrom the true value, that it is not clinically useful and is thereforeartifactual.

More sophisticated methods are available to perform “optimalestimation,” where the mean square errors of the individual sensors areeither known or estimated and a weighted average obtained which has nogreater mean square error than the best individual measurement. Weightedaveraging methods reduce the contribution from the worst sensors but donot solve the robustness problem, since erroneous sensor measurementsare still included in the final estimate.

In some examples, the problem of applying sensor fusion to physiologicdata in the clinical environment is unique from the other domains fortwo reasons. First, there are fewer sensors available for physiologicdata. Sensor fusion applied to physiological measurement in the clinicalenvironment will typically be based upon three, and sometimes two,sensors. As a result, for any given point in time, one or even zerosensors will be estimating the heart rate correctly. Therefore, thesolution must be designed to distinguish those situations where usefulinformation is available from one or more sensors or when no usefulinformation is available. Second, the sensors in robotics and radartracking tend to be susceptible to the same type of error. In contrast,the noise that can interfere with the physiologic sensor signals may befrequent or infrequent, of small or large magnitude and is alwaysunpredictable. In addition, different physiological sensors are affectedby different types of noise sources. Thus, the known sensor fusiontechniques of robotics and radar tracking are not effective forphysiological sensor fusion.

In some examples, since there are only a few sensors used in physiologicmeasurement, and any or all of the sensors may be incorrect at any time,consensus alone is not sufficient to reliably find the best estimate ofheart rate. In some examples, the present invention determines aconfidence level for every fused estimate. Whereas other methods ofsensor fusion base the confidence level only on consensus between themeasurements of the large number of sensors, the present inventiondetermines the confidence level based upon several factors, such asconsensus between sensors, the likelihood that a measurement isphysiologically consistent, and an understanding of the types of sensorerror that can occur. In addition, in some examples, the presentinvention goes to great lengths to detect artifact in the various sensormeasurements.

In some examples, the present invention is also adaptive in that theresults of the estimation process are used to refine the models whichdetermine physiologic consistency and acceptability of sensor error. Theinability to completely characterize the error leads to another uniqueaspect of the present invention. In some examples, worst case conditionsare always assumed when considering the possible error. For this reason,the present invention is considered “robust.” The term “robust,” as usedherein refers to signal processing systems that take statisticalunknowns or uncertainties into account and provide good systemperformance even in the face of these unknowns.

In some examples, an advantage of using multiple sensors is that theredundancy and diversity of sensors provides: (1) a greater likelihoodof having at least one source of correct information at all times; and(2) a means to decide upon the acceptability of a particular estimatebased upon the interrelationship between the sensor measurement. Thegreater likelihood of having at least one source of correct informationat all times (advantage 1 above) is apparent when one considers that, ifone sensor tends to be affected by a particular source of artifact, thenusing another sensor which is not affected in this way will improve thechances of having at least one accurate source and thus increases theprobability of generating a correct estimate of the physiologicalmeasurement.

In some examples, these principles can be extended to a situation wherethere are many sensors of diverse types, which provides the addedadvantage of determining the interrelationship between the varioussensor measurements (advantage 2 above) as a means of deciding whichmeasurements are accurate and which measurements are affected byartifact, since the likelihood of all or most of the sensors beingsimultaneously affected by some artifact is lower.

In some examples, the redundancy of multiple sensors aids in thisrespect since it allows the system to reject an observation based oneven a small possibility of artifact occurrence, since a more reliablealternative source will generally be available.

In some examples, the present invention is embodied in a system 2710illustrated in FIGS. 27-30 wherein a subject 2712 has a true heart rate,designated herein as .theta.. A plurality of sensors, designatedS.sub.1-S.sub.N, are coupled to the subject 2712. Each sensorS.sub.1-S.sub.N produces a different sensor measurement X.sub.1-X.sub.N,respectively, of the heart rate. In some examples, a sensor fusioncenter (SFC) 2718 analyzes each of the sensor measurementsX.sub.1-X.sub.N to derive a fused estimate FE of the heart rate. As willbe described in detail below, the SFC 2718 analyzes each of the sensormeasurements X.sub.1-X.sub.N using a priori knowledge of the sensorsS.sub.1-S.sub.N, as well as other statistical measures to derive thefused estimate FE.

It should be noted that the sensors S.sub.1-S.sub.N are not necessarilyof the same type and may, in fact, measure different quantities. Becausethe sensors S.sub.1-S.sub.N are contaminated by various types andextents of noise, they each provide sensor measurements that have anerror. For example, the sensor measurements X.sub.1-X.sub.2, illustratedin FIG. 28, are subject to nominal error contamination 2820 as well asartifactual error contamination 2822. Those of ordinary skill in the artunderstand that noise is present in virtually all physiologic signals.If the noise present on the sensors S.sub.1-S.sub.N is relatively lowlevel, the sensor observations X.sub.1-X.sub.N may contain only thenominal error contamination 2820, while large mounts of noise on thesensors can result in the artifactual error contamination 2822 in thesensor observations. The present invention is directed to the analysisof the error in the sensor observations X.sub.1-X.sub.N that resultsfrom the noise rather than any analysis of the noise itself.

In some examples, it is assumed that the nominal error contamination2820 is more prevalent in the sensor measurements X.sub.1-X.sub.N thanartifactual error contamination 2822. It is further assumed that thenominal error contamination 2820 is smaller than the maximum acceptableerror e. To ensure that these assumptions are valid, the value for themaximum acceptable error e, which may be user defined, should define arange that exceeds the acceptable nominal error contamination 2820. Thenominal error contamination 2820 arises from the cumulative effects ofmany different minor activities that derive from the subject 2712, thesubject-sensor interface, and signal processing errors. Therefore, insome examples, a mathematical principle known as the Central LimitTheorem justifies the characterization of the nominal errorcontamination 2820 by a fixed “white” Gaussian model or probabilitydensity function (PDF).

In contrast, the artifactual error contamination 2822 is expected toprovide a sporadic, but significant, impact on the sensor measurementsX.sub.1-X.sub.N. The artifactual error contamination 2822 is moredifficult to characterize statistically. Observations of heart rate datacan be used to indicate if the assumptions that nominal errorcontamination 2820 is more prevalent, but smaller than the maximumacceptable error, are valid. In addition, the maximum acceptable error,e, is used to define the sensor error as either nominal errorcontamination 2820 or artifactual error contamination 2822.

In some examples, the SFC 2718, 2918 utilizes the sensor measurementsX.sub.1-X.sub.N to provide a better estimate of heart rate than could beachieved by prior art systems that rely on an individual sensor. Sinceeach of the sensors S.sub.1-S.sub.N relies upon independent measurementsto determine heart rate, very often one or more sensors will beunaffected by the interference that influences another sensor's signal.In some examples, the goal of sensor fusion is to distinguish thosesensors which are afflicted with artifact from those which are not, andto produce a better estimate of heart rate by combining readings fromnon-afflicted sensors than is available from any single sensor.

There is an expected difference in the normal heart rate betweensubjects and for the same subject at different times. In some examples,the SFC 2718, 2918 uses past observations of heart rate to determinewhether current sensor measurements X.sub.1-X.sub.N are acceptable froma physiological perspective, because of the existence of finite limitson rate of change (i.e., velocity) as well as the rate of rate of change(i.e., acceleration), or even higher order differentials. The SFC 2718,2918 statistically characterizes this variability and utilizes thisstatistical knowledge of past observations to determine whether acurrent set of sensor measurements X.sub.1-X.sub.N are physiologicallyconsistent.

In some examples, the SFC 2718, 2918 uses the statistical data for heartrate variability in the particular subject to establish a model forheart rate variability for the particular subject. The heart ratevariability model can be used to determine whether the sensormeasurements X.sub.1-X.sub.N are consistent. It should also be notedthat the characteristics of heart rate vary from person to person. Anysensor measurements X.sub.1-X.sub.N that deviate from an expected “norm”based on these characteristics can provide an indication of theoccurrence of artifact or a significant change in the physiologicaldynamics of the subject, or both. In some examples, the SFC 2718, 2918utilizes a model of heart rate that incorporates specific informationabout the subject. As additional data is gathered from the individualsubject, the statistical model of heart rate variability is refined tobetter represent the characteristics of the particular subject.

In some examples, another basis on which to evaluate the credibility ofsensor measurements X.sub.1-X.sub.N involves knowledge about thecharacteristics of artifactual error contamination 2822 (see FIG. 28)which are endemic to the different types of sensors. For example, somesensors are more susceptible to certain types of physical movement,while others are affected by electrical interference such as anelectrocautery machine. Thus, some types of errors are more prevalentduring the occurrence of particular events, such as physical movement,or being subjected to electrosurgery. Furthermore, some types of errorstend to occur intermittently, while others can persist indefinitely.Even acceptable measurements are subject to some error, and knowledge ofthis type and extent of error, as well as the context of its occurrence,is useful in determining overall estimate of heart rate from a set ofacceptable readings. However, it is impossible to categorize or modelevery possible source, or combination of sources of artifact. At best,it is possible to model the sources of error which are acceptable andwhich sources of error tend to be common so that the occurrence of anunusual measurement will be detected based on its being unlikely tobelong to the model of acceptable errors.

In some examples, as illustrated in the functional block diagram of FIG.29, the SFC 2918 includes a Kalman filter circuit 2924 that provides aKalman filter KF.sub.1-KF.sub.N for each of the possible combination ofsensor measurements X.sub.1-X.sub.N. Kalman filters are statisticalfilters that are well known in the art and need only be discussedbriefly herein. However, numerous textbooks, such as Tracking and DataAssociation, by Y. Bar-Shalom and R. E. Fortmann, Academic Press,Boston, 1988, provide details on the theory and operation of Kalmanfilters and statistical signal processing and optimal estimation. TheKalman filter circuit 2924 includes individual Kalman filtersKF.sub.1-KF.sub.8 that are each supplied with the past fused estimateFE, as well as statistical data related to sensor error and variabilityof the parameter itself. It should be noted that the example of FIG. 29includes eight Kalman filters KF.sub.1-KF.sub.8 because eightcombinations of good or bad sensors are available from the three sensormeasurements X.sub.1-X.sub.3. However, the number of Kalman filtersdepends on the number of sensor measurements. With four sensors, a totalof sixteen Kalman filters are required.

In the example illustrated in FIG. 29, three sensors S.sub.1-S.sub.3produce sensor measurements X.sub.1-X.sub.3 that are either good or bad,depending on whether or not the particular observation was corrupted byartifactual error contamination 2822 (see FIG. 28). With three sensors,there are 8 (i.e., 2.sup.3) possible combinations of observations sinceeach sensor measurement X.sub.1-X.sub.3 may be providing either good orbad data.

Each possible sensor combination is known in the field of statistics asa “hypothesis.” It is not known which of the hypotheses is the bestestimate of the heart rate because it is not known which of the sensorscombinations contain data that has been affected by the artifactualerror contamination 2822. It should be noted that the Kalman filtersKF.sub.1-KF.sub.8 produce fused estimates for each hypothesis using onlysensor measurements that are assumed to be good for that particularhypothesis.

In some examples, each hypothesis is analyzed by a confidence calculator2926 to determine a confidence level for each hypothesis. As will bediscussed in detail below, the confidence level is a measure of theprobability of an accurate estimate given the likelihood ofcontamination by the nominal error contamination 2820 (see FIG. 28), thelikelihood of contamination by the artifactual error contamination 2822,as well as the likelihood of variation in the physiological parameteritself. The confidence calculator 2926 selects the hypothesis with thehighest confidence level as the fused estimate FE.

In some examples, the confidence calculator 2926 uses the fused estimateFE and determines an associated confidence level C for each fusedestimate. The fused estimate with the higher confidence value isselected as the optimal estimate. These parameters are used to modify asensor error model 2927 and a prediction error model 2928. In someexamples, the sensor error model 2927 is used to statisticallycharacterize the nominal error contamination 2820 on the sensormeasurements X.sub.1-X.sub.3. In some examples, the prediction errormodel 2928 is used to statistically characterize the variability of thephysiological parameter itself In the present example, the predictionerror model 2928 statistically characterizes the heart rate variability.The sensor error model 2927 and the prediction error model 2928 areupdated by the output of the confidence calculator 2926 and are used bythe Kalman filter circuit 2924 for the next set of measurements. Thus,the system 2710 is adaptive because the current results modify thesensor error model 2927 and the prediction error model 2928.

In some examples, the system 10 uses a three stage process, illustratedin the flow chart of FIG. 4, to arrive at the best estimate of heartrate at any point in time. At the start 30, the sensors S.sub.1-S.sub.Nhave provided sensor measurements X.sub.1-X.sub.N, respectively, to theSFC 18 (see FIG. 3). In step 32, the SFC 18 applies Kalman filtering toeach of the possible combinations of sensor measurements X.sub.1-X.sub.Nto determine the statistical probability of a valid reading from each ofthe combinations of sensor measurements. Thus, the Kalman filteringprocess produces the interim fused estimate IFE for each possiblecombination of sensor measurements X.sub.1-X.sub.N.

In some examples, in step 3036 the SFC 2718, 2918 determines theconfidence level for each interim fused estimate. The confidence levelis determined using Huber's theorem, which will be described in greaterdetail below. In step 3038, the SFC 2718, 2918 selects one interim fusedestimate as the fused estimate FE based on the confidence levels. Thesystem 2710 can select the interim fused estimate with the highestconfidence value. However, as will be described in detail below, theconfidence calculator 2926 (see FIG. 29) selects the interim fusedestimate with the highest probability of providing an accurate estimatein light of the probability of contamination by artifactual errorcontamination 2822 (see FIG. 28).

In some examples, in step 3040, the SFC 2718, 2918 refines the sensorerror model 2927 and the prediction error model 2928 to include the datafrom the current measurement. This adaptive aspect of the system 2710will be described in greater detail below. The system 2710 ends at step3044 with the interim fused estimate IFE with the highest confidencelevel having been selected as the fused estimate FE and models updatedto reflect the most current data. Thus, the system 2710 can derive anaccurate measure of the physiological parameter from the various sensormeasurements X.sub.1-X.sub.N even in the presence of artifactual errorcontamination 2822 (see FIG. 28).

The various stages of processing by the SFC 2718, 2918 will now bedescribed in detail. Returning to FIG. 29, stage one of the analysis,which corresponds to step 3032 (see FIG. 30), involves the Kalmanfilters KF.sub.1-KF.sub.8, which utilize the statistical characteristicsof sensor data to derive the optimal heart rate estimate for everypossible combination of sensor states, which is either artifactual ornon-artifactual. Statistical characteristics are essentially astatistical description of the expected physiologic changes in heartrate that can be measured by the sensor as well as a statisticaldescription of the types of error that can corrupt the measurement.

In some examples, each of the Kalman filters KF.sub.1-KF.sub.8 producesan interim fused heart rate estimate .theta..sub.1-.theta..sub.8,respectively and a value P.sub.1-P.sub.8, respectively, which are theerror covariance extrapolation values. These values are based on thepast fused heart rate estimate, designated herein as .theta.. sup.-, andthe statistical characterizations of the sensor error model 2927 and theprediction error model 2928. The values .theta..sub.1-.theta..sub.8represent the interim fused estimate for each of the eight hypotheses,respectively. The values P.sub.1-P.sub.8, designated as error covarianceextrapolation values, are conventional values generated by Kalmanfilters to estimate the error that results from using the past fusedestimate .theta..sup.- to generate the current estimates.theta..sub.1-.theta..sub.8. That is, the Kalman filtersKF.sub.1-KF.sub.8 have some error that is attributable to the fact thatthe present estimate is based in part on a past estimate of theparameter rather than an exact value of the parameter. Those familiarwith Kalman filtering will understand that the values P.sub.1-P.sub.8should not be confused with error covariance that is generated afterincorporating the current sensor measurements X.sub.1-X.sub.8. The errorcovariance corresponding to the chosen hypothesis is then used togenerate the error covariance extrapolation values P.sub.1-P.sub.8 forthe next set of measurements.

In some examples, the error resulting from the use of the past fusedestimate .theta..sup.- in the Kalman filter circuit 2924 to generate thecurrent estimates .theta..sub.1-.theta..sub.8 are well known to those ofordinary skill in the art of statistical analysis and signal processingand need not be explained in greater detail herein. As will be describedin greater detail below, the values P.sub.1-P.sub.8 are used by theconfidence calculator 2926 to determine the confidence level for eachhypothesis.

In some examples, the values P.sub.1-P.sub.8 are set to a large valuewhen the system 2710 is initialized to reflect the fact that them is nopast fused estimate .theta..sup.-. As past estimates become available,the values of P.sub.1-P.sub.8 become smaller to reflect the fact thatthe present interim fused estimates .theta..sub.1-.theta..sub.8 areestimated more reliably as the system 2710 adapts to the individualsubject. As previously discussed, the system 2710 dynamically adapts tothe specific individual as more measurements are performed.

In some examples, as discussed above, the current interim fusedestimates .theta..sub.1-.theta..sub.8 by the Kalman filtersKF.sub.1-KF.sub.8 are based in part on the past fused estimate.theta..sup.-. Those familiar with Kalman filtering will understand thatmore than one past estimate can be used to derive a past estimate value.For example, an average value for a number of past estimates could beused. However, in its presently preferred embodiment, the system 2710uses only the immediate past fused estimate .theta..sup.- as an input tothe Kalman filters KF.sub.1-KF.sub.8.

In some examples, the Kalman filter provides an optimal estimate if aGaussian distribution PDF characterizes the nominal error contamination2820 (see FIG. 28), as previously discussed. The Gaussian PDF for thenominal error contamination 2820, represented by the sensor error model2927, is completely characterized by the sensor measurementsX.sub.1-X.sub.8 and R, which is a correlation matrix whose elements arenominal sensor error variances and cross-covariances. In some examples,in the present embodiment of the system 2710, the values of thecross-covariances are set to zero based on the assumption that theerrors are independent. However, as those skilled in the art canappreciate, it is possible to determine values of the cross-covariances.The characterization of the Gaussian distribution PDF of the nominalerror contamination 2820 using the matrix R is well known in the fieldof statistical signal processing and need not be discussed in greaterdetail herein.

In addition, in some examples, the system 2710 assumes that nominalchanges in heart rate can also be represented by a Gaussian PDF. TheGaussian PDF for the heart rate variability, represented by theprediction error model 2928, is also assumed to be completelycharacterized by the past fused estimate .theta..sup.- and a value Q,which is a variability correlation estimate that describes the nature ofthe Gaussian distribution for the nominal changes in heart rate. Thecharacterization of the Gaussian distribution PDF of the heart ratevariability using the value Q is well known in the field of statisticalsignal processing and need not be discussed in greater detail herein.For example, the use of Kalman filters and the estimators R, P, and Qare discussed in Applied Optimal Estimation edited by Arthur Gelb, M. I.T. Press, Cambridge, Mass., 1974.

In some examples, the Kalman filters KF.sub.1-KF.sub.8 are eachfirst-order Kalman filters using the sensor measurementsX.sub.1-X.sub.3, the past fused estimate .theta..sup.- for heart rate,and the correlations R and Q. The system 2710 initially assumes worstcase values for R and Q. As will be described below, the estimates for Rand Q are dynamically updated as the system 2710 continues to makemeasurements. The dynamic updating makes the system 2710 adaptive. Thus,the Kalman filters KF.sub.1-KF.sub.8 produce eight interim fusedestimates .theta..sub.1-.theta..sub.8 of heart rate using thestatistical knowledge of error and changes in heart rate. The bestestimate, and its corresponding confidence level are determined in thenext stage of analysis.

In some examples, as previously discussed, the Central Limit Theoremjustifies the nominal error contamination to be characterized as aGaussian distribution. However, the artifactual error contamination 2822(see FIG. 28) can have any statistical distribution of error. Therefore,to maximize accuracy, the system 2710 does not make any a prioriassumptions about the PDF of the artifactual error contamination 2822.While the Kalman filter circuit 2924 can use the Gaussian PDFs for thenominal error contamination 2820 (see FIG. 28) and the heart ratevariability, it does not take into account any probability ofartifactual error. This probability is taken into account by theconfidence calculator 2926.

In some examples, stage two of the analysis, which corresponds to steps3036 and 3038 of FIG. 30, uses Huber's theorem for posterior robustnessto find a confidence level for each interim fused estimate.theta..sub.1-.theta..sub.8 obtained from the Kalman filtersKF.sub.1-KF.sub.8. Huber's theorem for posterior robustness is wellknown and need not be discussed in detail herein. The fundamentals ofHuber's theorem are discussed in Statistical Decision Theory andBayesian Analysis, Berger, J. O., Springer Verlag, Second Edition, 1985.The application of Huber's theorem to statistical signal processing forsensor fusion of physiologic signals will be discussed in detail below.The calculated confidence level ranges in value from 0.0 to 1.0 andexpresses the likelihood that the associated interim fused estimate iscorrect.

In some examples, the confidence level is determined by the confidencecalculator 2926 based on several factors. The physiologic credibility ofthe estimate is the first factor. Physiologic credibility is used toevaluate the interim fused estimate .theta..sub.1-.theta..sub.8 relativeto the known limits of heart rate, both absolute heart rate limits andlimits on the rate of change of the heart rate. Consistency of theinterim fused estimates .theta..sub.1-.theta..sub.8 with the parametervariability model 2928 is a second factor used by the confidencecalculator 2926 to determine the confidence level. Consensus between thesensors S.sub.1-S.sub.3 is a third factor used to determine theconfidence level. The confidence level is increased when more than onesensor is in agreement. Statistical descriptions of sensor error arethen evaluated and worst case assumptions about artifactual errorcharacteristics are used to derive the confidence level.

In some examples, the calculation of this probability can be done bydirect application of the classical statistical relationship, BayesTheorem, which is well known, and will not be discussed in detailherein. Bayes Theorem and its application and statistics are discussedin statistical decision and Bayesian Analysis, as well as Introductionto the Theory of Statistics by Mood et at., McGraw Hill, 3d ed., 1985.As those skilled in the art will appreciate, equation (6) below is aderivation for the application of Bayes Theorem to the estimation of aparameter. Such a derivation is discussed in Statistical Decision Theoryand Bayesian Analysis (page 83). It is also assumed that thephysiological parameter of interest can be any value, with equalprobability, within that range. It should be noted that the f PDF isonly partially characterized by the sensor error model 2927 since thesensor error model only characterizes the nominal error contamination2820 (see FIG. 28). As will be discussed in detail below, the completecharacterization of the f PDF requires the consideration of theartifactual error contamination 2822 (see FIG. 2). As previouslydiscussed, the values P.sub.1-P.sub.8 generated by the Kalman filtersKF.sub.1-KF.sub.8, respectively, are taken into account by theconfidence calculator 2926. The values P.sub.1-P.sub.8 are the variancesof the g PDF and characterize the prediction error that results from theuse of past estimates rather than exact values of the physiologicparameter.

In some examples, in order to implement the f PDF given by the sensorerror model 2927 (see FIG. 29) and the g PDF given by the predictionerror model 2928 are required. Recall that the Kalman Filter only usesthe prediction error model 2928, characterized by past fused estimate.theta.. sup.- and the value Q, and the sensor error model 2927 fornominal error contamination 2820 (see FIG. 2), characterized by matrixR. The prediction error model 2928 is available, but the sensor errormodel 2927 is incompletely specified, since the artifactual error modelto characterize artifactual error contamination 2822 (see FIG. 28) isnot known. The nominal and the artifactual error models are relatedthrough an “epsilon class contamination” model.

In some examples, the fundamental concepts of the epsilon classcontamination are known in the art and will not be discussed in detailherein. Such models are discussed, for example, in Statistical DecisionTheory and Bayesian Analysis. Assume that the error contamination willeither be the nominal error contamination 2820 (see FIG. 28), which ischaracterized by the Gaussian PDF, or the artifactual errorcontamination 2822, which has an unknown PDF. The system 2710 assumesthat the selection process is biased in favor of the nominal errorcontamination 2820 by a probability (1-.epsilon..sub.hyp). Thus, thetrue error model is the composite of the Gaussian PDF for the nominalerror contamination 2820 and the unknown model error for the artifactualerror contamination 2822. The composite of these two models is termed a“heavy-tailed” PDF, and has been widely applied in the field of robuststatistics.

In some examples, by making worst case assumptions about the nature ofthe artifactual model, the resulting probability value yields a robustchoice of the correct hypothesis. The design is robust because itaccounts for the unknown artifactual model by assuming worst casepossibilities and yet also produces feasible performance. The onlydifference is the assumption of worst-case artifactual error type whichyields the minimum probability of achieving an estimate within thetolerance. So the selection process now consists of picking the mostlikely (maximum probability hypothesis) from among the minimumprobabilities.

In some examples, the implementation can be carried out by applying theepsilon class contamination model to the implementation of the BayesFormula, and assuming worst case characteristics for the unknown errormodel. Details of the derivation can be found in Statistical DecisionTheory and Bayesian Analysis, page 211, and are thus omitted here. Theworst case analysis was first carried out by Peter Huber and is thusknown as Huber's theorem. The value of y can be set by the user toselect the weighting of consensus among the sensor measurementsX.sub.1-X.sub.N. This can range from a value requiring completeagreement among the sensor measurements X.sub.1-X.sub.N to requiringconsensus within a specified error range. In some examples, the system2710 is based on the premise that consensus means that there is noartifactual error contamination 2822 (see FIG. 28) in the sensormeasurements X.sub.1-X.sub.N.

In some examples, for the hypotheses where only one of the sensorsS.sub.1-S.sub.N is assumed to be good, consensus cannot be used toremove doubt about the occurrence of artifact, so the worst caseconfidence is derived by setting y=1. Where consensus can be used toremove doubt that artifacts are involved, the formula for setting y isdependent upon the user's discretion about the type of artifactual errorthat is encountered.

In some examples, by employing Huber's theorem, the unspecifiedartifactual model is also accounted for by determining the highestlikelihood (assuming the “worst” artifactual model characteristic) thatthe estimate is outside the accepted tolerance. This calculation alsodepends very strongly upon both the epsilon value and the predictionerror model 2928. If the epsilon value is negligibly small, then thelikelihood of artifact is correspondingly small. If the prediction errormodel 2928 is such that the parameter cannot change beyond theacceptable tolerance from one reading to the next, then the likelihoodof artifact is also small. In such circumstances, the regular Bayesianmodel can be used, since it is adequate for deciding if a particularreading is contaminated by the artifactual error contamination 2822 evenwithout consensus among the sensors S.sub.1-S.sub.N.

In some examples, one final unknown that remains to be considered arethe epsilon values, which are indicative of the probability ofcontamination by the artifactual error contamination 2822 (see FIG. 2).If the probability of contamination by the artifactual errorcontamination 2822 of each of the sensors S.sub.1-S.sub.N remainedalmost the same from case to case, then the epsilon values could beestimated from the vast quantity of data. But analyses of sensorperformance data reveals that this is not the case. To account for theunknown probability of artifact, the system 2710 treats each of thesensors S.sub.1-S.sub.N as being equally susceptible to contamination bythe artifactual error contamination 2822. Instead of merely findingwhich hypothesis has the highest probability of being correct, thesystem 2710 finds the hypothesis that can withstand the highest a prioriprobability of artifactual error contamination 2822 and still be abetter option, in which all sensors are contaminated by the artifactualerror contamination.

To achieve this goal, in some examples, the confidence calculator 2926recalculates the confidence level to be the maximum a priori probabilityof contamination by the artifactual error contamination 2822 (maximumepsilon) such that the minimum probability of being correct, will exceedthe probability of sensor combination 8 being the correct hypothesis. Itshould be noted that sensor combination 8 is the only hypothesis thatdoes not incorporate sensor measurements X.sub.1-X.sub.3 in theestimation process at all because sensor combination 8 assumes allsensor measurements are affected by the artifactual error contamination2822. Thus, the fused estimate from sensor combination 8 is based solelyon statistical prediction and not on any current sensor measurementsX.sub.1-X.sub.8.

In some examples, the system 2710 measures the confidence level for eachof the hypotheses in a manner that takes into account the worst casescenarios in terms of probabilities of artifact and artifactual errormodels. This permits the system 2710 to select the best estimate andcompute its corresponding confidence level. The system 2710 usesaccurate models of parameter variability and sensor error to obtaindiscriminating measures of confidence even under such difficultassumptions. The accuracy of the fused estimate FE comes from theability of the system 2710 to use consensus to help in achieving highconfidence estimates, which can be used to refine the models from caseto case, and to adapt them from moment to moment. As those skilled inthe art can appreciate, this accuracy is difficult to achieve in thepresence of high levels of contamination by the artifactual errorcontamination 2822 and significant physiologic differences from case tocase if only one sensor is available. Thus, the confidence calculator2826 of system 2710 produces the fused heart rate estimate FE as well asa confidence level indicative of the confidence of the system in thefused estimate FE. As previously discussed, the system 2710 is adaptivebecause the results of the current measurement are used to modify thesensor error model and the prediction error model.

In some examples, the fused estimate FE and the confidence levelgenerated by the confidence calculator 2926 are used to refine theparameters R and Q, which characterize the Gaussian PDF of the sensorerror model 2927 and the Gaussian PDF of the prediction error model2928, respectively. As previously discussed, the correlation values Rand Q are required to implement the Kalman filters KF.sub.1-KF.sub.8 andto compute the confidence levels. Ideally, the variable Q is defined asthe variance of the variability of the physiological parameter. However,it is impossible to determine the true value for Q because the currenttrue heart rate value .theta. and the past true heart rate value.theta.. sup.- are not known. The current and past heart rate values canonly be estimated and may contain errors. Therefore, the value for Q canonly be estimated. This estimate is shown herein by the value “Q” whichindicates that the value is an estimate of Q.

In essence, the estimate of Q, which is the average square “nominal”change in the parameter from moment to moment. The variability factorchanges from case to case, and changes slowly over time.

The adaptive estimate is simply a moving average of accurate parametervariations. It should be noted that the rate of adaptation depends uponthe size of the user-defined adaptation constant .rho..sub.Q. In someexamples, if the adaptation constant .rho..sub.Q is high (approaching 1)then the latest estimate of rate of change replaces the previousestimate completely. On the other hand, if the adaptation constant.rho..sub.Q is low (approaching 0), then there is no change in theestimate of Q based upon the latest estimates. As those skilled in theart can appreciate, the optimal setting of the constant depends upon thevariability of Q. Since this is also an unknown, a reasonablyconservative setting where Q varies slowly is applied. The Kalmanfilters KF.sub.1-KF.sub.8 provide the interim fused estimates.theta..sub.1-.theta..sub.8, respectively based on a weighting of thesensor measurements X.sub.1-X.sub.3, the past fused estimate .theta..sup.-, and the values Q and R. In some examples, elements that areconsidered less reliable are given less weighting by the Kalman filtersKF.sub.1-KF.sub.8. In practice, Q, which is a measure of the variance ofthe heart rate variability, will be larger than the elements of R, andwill thus be weighed considerably less than the sensor values inarriving at a Kalman estimate. The value for Q is initialized as thesquare of the highest possible change, from sample to sample, thatphysiologic credibility will allow. The ideal R value is a matrix thatrepresents the variance of the sensor error from sensor measurementsX.sub.1-X.sub.3 deemed to be good (i.e., not affected by the artifactualerror contamination 2822).

The nominal error variance is expected to vary from case to case, but itis not expected to vary over time. As a result, a recursive rather thanadaptive algorithm may be used to estimate these elements. The recursiveestimate is equivalent to a batch average of accurate sensor errorestimates, which approaches the true value as more samples are used inits computation. The accuracy in the sensor error estimates is dependentupon the number of sensors fused. In order to estimate the nominal errorvalues, only in cases when the accepted hypotheses involve more than onesensor that is within the limits of tolerance, an update is carried out.

In some examples, the system 2710 can adaptively estimate the value Qwhich characterizes sensor variability on-line and recursively estimatethe values R which characterizes nominal sensor error on-line. Both thevalues Q and R are essential to obtaining optimal Kalman estimates ofthe parameter. The values R and Q are also essential to obtaining highlydiscriminating confidence levels to select the best estimate and providea corresponding confidence measurement. The system 2710 can be readilyincorporated into a conventional digital computer (not shown). However,the data processing power of a digital signal processor (not shown) cangreatly enhance the overall performance of the system 2710. The presentinvention is not limited by the particular platform on which the system2710 is implemented.

It is to be understood that even though various embodiments andadvantages of the present invention have been set forth in the foregoingdescription, the above disclosure is illustrative only, and changes maybe made in detail, yet remain within the broad principles of theinvention. Therefore, the present invention is to be limited only by theappended claims.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” Such examples can include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects. The terms approximately, about or substantially aredefined herein as being within 10% of the stated value or arrangement.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherexamples can be used, such as by one of ordinary skill in the art uponreviewing the above description. The Abstract is provided to allow thereader to quickly ascertain the nature of the technical disclosure. Itis submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed example. Thus, the following claims are herebyincorporated into the Detailed Description as examples or examples, witheach claim standing on its own as a separate example, and it iscontemplated that such examples can be combined with each other invarious combinations or permutations. The scope of the invention shouldbe determined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

NOTES AND VARIOUS EXAMPLES

Each of these non-limiting examples may stand on its own, or may becombined in various permutations or combinations with one or more of theother examples. The examples are supported by the preceding writtendescription as well as the drawings of this disclosure.

Example 1 is an automated data consolidation module including a modulefor housing one or more of electronic and electromechanical medicalequipment and a system to receive and analyze the data produced by theelectronic and electromechanical medical equipment using sensor fusiontechniques, the automated data consolidation module comprising: ahousing configured to house the electronic and electromechanical medicalequipment; a cowling that substantially confines the one or more ofelectronic and electromechanical medical equipment; processing circuitryin electrical communication with the electronic and electromechanicalmedical equipment to receive dose event and response event digital dataproduced by the electronic and electromechanical medical equipment; anda system for estimation of a physiological parameter of a subject usingsensor fusion techniques, the system comprising: a plurality of sensorscoupled to the subject, each providing a signal capable of producing aphysiological parameter measurement; a filter circuit receiving saidphysiological parameter measurements and producing a fused physiologicalparameter estimate for each possible combination of said physiologicalparameter measurements from said plurality of sensors in which each ofsaid physiological parameter measurements in said combinations isconsidered to be acceptable or unacceptable; and a confidence calculatorcoupled to said filter circuit to receive said fused physiologicalparameter estimates and to determine a confidence level value for eachof said fused physiological parameter estimates indicative of anaccuracy of each of said fused physiological parameter estimates, saidconfidence calculator selecting said one of said fused physiologicalparameter estimates as the estimated physiological parameter based onsaid confidence level.

In Example 2, the subject matter of Example 1 optionally includeswherein said filter circuit is a Kalman filter for each of said possiblecombinations, said Kalman filter circuit using past physiologicalparameter estimates and a statistical measure of contamination of saidphysiological parameter measurements by nominal error to produce saidfused physiological parameter estimates.

In Example 3, the subject matter of Example 2 optionally includeswherein said statistical measure of nominal error contamination includesa Gaussian probability density function of said nominal errorcontamination, said Kalman filter using said Gaussian probabilitydensity function to produce said fused physiological parameterestimates.

In Example 4, the subject matter of any one or more of Examples 1-3optionally include wherein said filter circuit includes a statisticalfilter and a statistical measure of physiological parameter variability,said filter circuit using said statistical measure of physiologicalparameter variability to produce said fused physiological parameterestimates.

In Example 5, the subject matter of Example 4 optionally includeswherein statistical measure of physiological parameter variability is aGaussian probability density function of physiological parametervariability, said filter circuit using said Gaussian probability densityfunction of physiological parameter variability to produce said fusedphysiological parameter estimates.

In Example 6, the subject matter of any one or more of Examples 1-5optionally include wherein said physiological parameter measurements aresusceptible to contamination by artifactual error, said contaminatedphysiological parameter measurements producing an unacceptablephysiological parameter measurement, said confidence calculatorperforming a statistical analysis of said fused physiological parameterestimates to determine a statistical probability of contamination ofeach of said fused physiological parameter estimates by said artifactualerror.

In Example 7, the subject matter of Example 6 optionally includeswherein said filter circuit is a statistical filter and includes astatistical measure of nominal error contamination affecting saidphysiological parameter measurements, said confidence calculatorcombining said statistical measure of nominal error contamination andsaid statistical probability of contamination of each of said fusedphysiological parameter estimates by said artifactual error to determinesaid selected fused physiological parameter estimate.

In Example 8, the subject matter of any one or more of Examples 6-7optionally include wherein said confidence calculator calculates aminimum confidence value for one of said measurement combinations forwhich all of the physiological signals are assumed to be contaminated bysaid artifactual error, said confidence calculator further calculating aconfidence value for remaining measurement combinations using aworst-case probability of contamination by said artifactual error whilealso having a minimum probability of said confidence level exceedingsaid minimum confidence level, said selected fused physiologicalparameter estimate having the greatest confidence value.

In Example 9, the subject matter of any one or more of Examples 1-8optionally include a statistical model for physiological parametervariability, said confidence calculator using said statistical model todetermine said confidence level and modifying said statistical modelfollowing the selection of said selected fused physiological parameterestimate.

In Example 10, the subject matter of any one or more of Examples 1-9optionally include a statistical model for sensor error to characterizesusceptibility of said sensors to nominal error contamination, saidconfidence calculator using said statistical model to determine saidconfidence level and modifying said statistical model following saidselection of said selected fused physiological parameter estimate.

Example 11 is an automated data consolidation module including a modulefor housing one or more of electronic and electromechanical medicalequipment and a system to receive and analyze the data produced by theelectronic and electromechanical medical equipment using sensor fusiontechniques, the automated data consolidation module comprising: ahousing configured to house the electronic and electromechanical medicalequipment; a cowling that substantially confines the one or more ofelectronic and electromechanical medical equipment; processing circuitryin electrical communication with the electronic and electromechanicalmedical equipment to receive dose event and response event digital dataproduced by the electronic and electromechanical medical equipment; anda system for fusion of physiological sensor measurements in a subjecthaving a plurality of sensors coupled thereto, each of the sensorsproducing a sensor measurement related to a physiological parameter, thesystem comprising: a statistical model affecting the fusion of thesensor measurements; a statistical filter circuit receiving the sensormeasurements and said statistical model and producing a fused parameterestimate for each possible combination of the sensor measurements fromthe plurality of sensors in which each of the sensor measurements insaid combinations are considered to be acceptable or unacceptable; and aconfidence calculator coupled to said filter circuit to receive saidfused parameter estimates and to determine a confidence level for eachof said fused parameter estimates indicative of an accuracy of each ofsaid fused parameter estimates, said confidence calculator selecting oneof said fused parameter estimate as a final parameter estimates based onsaid confidence level.

In Example 12, the subject matter of Example 11 optionally includeswherein said filter circuit also receives a previous estimate of thefinal parameter estimate selected by said confidence calculator, saidfilter circuit producing said fused parameter estimates based on thesensor measurements, said statistical model, and said previous estimate.

In Example 13, the subject matter of any one or more of Examples 11-12optionally include wherein said statistical model is a parametervariability statistical model characterizing changes in the parameterover time and said filter circuit produces said parameter estimatesusing said parameter variability statistical model.

In Example 14, the subject matter of Example 13 optionally includes asensor measurement error statistical model characterizing susceptibilityof the sensor measurements to nominal error contamination, said filtercircuit producing said fused parameter estimates using said parametervariability statistical model and said sensor error statistical model.

In Example 15, the subject matter of any one or more of Examples 11-14optionally include wherein said statistical model is a sensormeasurement error statistical model characterizing susceptibility of thesensor measurements to nominal error contamination, said filter circuitproducing said parameter estimates using said sensor error statisticalmodel.

In Example 16, the subject matter of Example 15 optionally includes aparameter variability statistical model characterizing changes in theparameter over time, said filter circuit producing said parameterestimates using said sensor error statistical model and said parametervariability statistical model.

In Example 17, the subject matter of any one or more of Examples 11-16optionally include wherein said confidence calculator alters saidstatistical model following the selection of said parameter estimate,whereby the system is adaptive by using said final parameter estimate toalter said statistical model.

In Example 18, the subject matter of any one or more of Examples 11-17optionally include wherein said filter circuit is a Kalman filter foreach of said possible combinations, each of said Kalman filters using aprevious parameter estimate and said statistical model to produce saidfused parameter estimates.

In Example 19, the subject matter of Example 18 optionally includeswherein said statistical model is a sensor measurement error statisticalmodel having a Gaussian probability density function characterizingsusceptibility of the sensor measurements to nominal errorcontamination, said Kalman filter using said Gaussian probabilitydensity function to produce said fused parameter estimates.

In Example 20, the subject matter of any one or more of Examples 18-19optionally include wherein said statistical model is a parametervariability model having a Gaussian probability density functioncharacterizing changes in the parameter over time, said Kalman filterusing said Gaussian probability density function to produce said fusedparameter estimates.

In Example 21, the subject matter of any one or more of Examples 11-20optionally include wherein the automated data consolidation module isconfigured to be used with physiological sensor measurements that aresusceptible to contamination by artifactual error, said contaminatedphysiological sensor measurements being considered unacceptable, saidconfidence calculator analyzing said fused parameter estimates todetermine a statistical probability of error of each of said fusedparameter estimates caused by said artifactual error.

In Example 22, the subject matter of Example 21 optionally includeswherein said statistical model is a sensor error statistical modelcharacterizing the susceptibility of the sensor measurements to nominalerror contamination, said confidence calculator combining said sensorerror statistical model and said statistical probability ofcontamination of each of said fused parameter estimates by saidartifactual error to determine said final parameter estimate.

In Example 23, the subject matter of any one or more of Examples 21-22optionally include wherein said confidence calculator calculates aminimum confidence value for one of said measurement combinations forwhich all of the physiological sensor measurements are consideredunacceptable, said confidence calculator further selecting calculating aconfidence value for remaining measurement combinations using aworst-case probability of contamination by said artifactual error whilealso having a minimum probability of said confidence level exceedingsaid minimum confidence level, said confidence calculator selecting assaid final parameter estimate the fused parameter estimate with amaximum confidence value.

Example 24 is an automated data consolidation module including a modulefor housing one or more of electronic and electromechanical medicalequipment and a method to receive and analyze the data produced by theelectronic and electromechanical medical equipment using sensor fusiontechniques, the automated data consolidation module comprising: ahousing configured to house the electronic and electromechanical medicalequipment; a cowling that substantially confines the one or more ofelectronic and electromechanical medical equipment; processing circuitryin electrical communication with the electronic and electromechanicalmedical equipment to receive dose event and response event digital dataproduced by the electronic and electromechanical medical equipment; anda method for fusion of physiological sensor measurements in a subjecthaving a plurality of sensors coupled thereto, each of the sensorsproducing a sensor measurement related to a physiological parameter, themethod comprising: generating a statistical model affecting the fusionof the sensor measurements; producing a fused parameter estimate basedon the sensor measurements and said statistical model for each possiblecombination of the sensor measurements from the plurality of sensors inwhich each of the sensor measurements in said combinations areconsidered to be acceptable or unacceptable; determining a confidencelevel value for each of said fused parameter estimates indicative of anaccuracy of each of said parameter estimates; and selecting a finalparameter estimate based on said confidence level.

In Example 25, the subject matter of Example 24 optionally includeswherein producing said fused parameter estimates includes basing saidfused parameter estimates on the sensor measurements, said statisticalmodel, and a previous final parameter estimate.

In Example 26, the subject matter of any one or more of Examples 24-25optionally include wherein: said statistical model is a parametervariability statistical model characterizing changes in the parameterover time; and producing said fused parameter estimates includes usingsaid parameter variability statistical model.

In Example 27, the subject matter of Example 26 optionally includesgenerating a sensor measurement error statistical model characterizingsusceptibility of the sensor measurements to nominal errorcontamination, wherein producing said fused parameter estimates includesusing said parameter variability statistical model and said sensor errorstatistical model.

In Example 28, the subject matter of any one or more of Examples 24-27optionally include wherein: said statistical model is a sensormeasurement error statistical model characterizing susceptibility of thesensor measurements to nominal error contamination; and producing saidfused parameter estimates uses said sensor measurement error statisticalmodel.

In Example 29, the subject matter of Example 28 optionally includesgenerating a parameter variability statistical model characterizingchanges in the parameter over time, wherein producing said fusedparameter estimates uses said sensor measurement error statistical modeland said parameter variability statistical model.

In Example 30, the subject matter of any one or more of Examples 24-29optionally include wherein producing said fused parameter estimatesincludes using a Kalman filter for each of said possible combinationsand using a previous final parameter estimate and said statistical modelin said Kalman filters to produce said fused parameter estimates.

1. An automated data consolidation module including a module for housingtwo or more pieces of electronic and electromechanical medical equipmentand a system to receive and analyze the data produced by the electronicand electromechanical medical equipment using sensor fusion techniques,the automated data consolidation module comprising: a housing configuredto house the electronic and electromechanical medical equipment; acowling that substantially confines the one or more of electronic andelectromechanical medical equipment; processing circuitry in electricalcommunication with the electronic and electromechanical medicalequipment to receive dose event and response event digital data producedby the electronic and electromechanical medical equipment; and softwareutilizing sensor fusion components that automatically compare responseevent data produced by the electronic and electromechanical medicalequipment to determine if the data from one of the two or moreelectronic and electromechanical medical equipment is erroneous; whereinthe sensor fusion components comprise: a plurality of sensors coupled tothe subject, each providing a signal capable of producing aphysiological parameter measurement; a filter circuit receiving saidphysiological parameter measurements and producing a fused physiologicalparameter estimate for each possible combination of said physiologicalparameter measurements from said plurality of sensors in which each ofsaid physiological parameter measurements in said combinations isconsidered to be acceptable or unacceptable; and a confidence calculatorcoupled to said filter circuit to receive said fused physiologicalparameter estimates and to determine a confidence level value for eachof said fused physiological parameter estimates indicative of anaccuracy of each of said fused physiological parameter estimates, saidconfidence calculator selecting said one of said fused physiologicalparameter estimates as the estimated physiological parameter based onsaid confidence level.
 2. The automated data consolidation module ofclaim 1, wherein said filter circuit is a Kalman filter for each of saidpossible combinations, said Kalman filter circuit using pastphysiological parameter estimates and a statistical measure ofcontamination of said physiological parameter measurements by nominalerror to produce said fused physiological parameter estimates.
 3. Theautomated data consolidation module of claim 2, wherein said statisticalmeasure of nominal error contamination includes a Gaussian probabilitydensity function of said nominal error contamination, said Kalman filterusing said Gaussian probability density function to produce said fusedphysiological parameter estimates.
 4. The automated data consolidationmodule of claim 1, wherein said filter circuit includes a statisticalfilter and a statistical measure of physiological parameter variability,said filter circuit using said statistical measure of physiologicalparameter variability to produce said fused physiological parameterestimates.
 5. The automated data consolidation module of claim 4,wherein statistical measure of physiological parameter variability is aGaussian probability density function of physiological parametervariability, said filter circuit using said Gaussian probability densityfunction of physiological parameter variability to produce said fusedphysiological parameter estimates.
 6. The automated data consolidationmodule of claim 1, wherein said physiological parameter measurements aresusceptible to contamination by artifactual error, said contaminatedphysiological parameter measurements producing an unacceptablephysiological parameter measurement, said confidence calculatorperforming a statistical analysis of said fused physiological parameterestimates to determine a statistical probability of contamination ofeach of said fused physiological parameter estimates by said artifactualerror.
 7. The automated data consolidation module of claim 6, whereinsaid filter circuit is a statistical filter and includes a statisticalmeasure of nominal error contamination affecting said physiologicalparameter measurements, said confidence calculator combining saidstatistical measure of nominal error contamination and said statisticalprobability of contamination of each of said fused physiologicalparameter estimates by said artifactual error to determine said selectedfused physiological parameter estimate.
 8. The automated dataconsolidation module of claim 6, wherein said confidence calculatorcalculates a minimum confidence value for one of said measurementcombinations for which all of the physiological signals are assumed tobe contaminated by said artifactual error, said confidence calculatorfurther calculating a confidence value for remaining measurementcombinations using a worst-case probability of contamination by saidartifactual error while also having a minimum probability of saidconfidence level exceeding said minimum confidence level, said selectedfused physiological parameter estimate having the greatest confidencevalue.
 9. The automated data consolidation module of claim 1, furthercomprising a statistical model for physiological parameter variability,said confidence calculator using said statistical model to determinesaid confidence level and modifying said statistical model following theselection of said selected fused physiological parameter estimate. 10.The automated data consolidation module of claim 1, further comprising astatistical model for sensor error to characterize susceptibility ofsaid sensors to nominal error contamination, said confidence calculatorusing said statistical model to determine said confidence level andmodifying said statistical model following said selection of saidselected fused physiological parameter estimate.
 11. An automated dataconsolidation module including a module for housing two or more piecesof electronic and electromechanical medical equipment and a system toreceive and analyze the data produced by the electronic andelectromechanical medical equipment using sensor fusion techniques, theautomated data consolidation module comprising: a housing configured tohouse the two or more pieces of electronic and electromechanical medicalequipment, the two or more pieces of electronic and electromechanicalmedical equipment each having one or more sensors; a cowling thatsubstantially confines the two or more pieces of electronic andelectromechanical medical equipment; processing circuitry in electricalcommunication with the electronic and electromechanical medicalequipment to receive dose event and response event digital data producedby the electronic and electromechanical medical equipment; and a systemfor fusion of response event physiological sensor measurements in asubject having a plurality of sensors coupled thereto in order todetermine if one of the sensors is erroneous, each of the sensorsproducing a sensor measurement related to a physiological parameter, thesystem comprising: a statistical model affecting the fusion of thesensor measurements; a statistical filter circuit receiving the sensormeasurements and said statistical model and producing a fused parameterestimate for each possible combination of the sensor measurements fromthe plurality of sensors in which each of the sensor measurements insaid combinations are considered to be acceptable or unacceptable; and aconfidence calculator coupled to said filter circuit to receive saidfused parameter estimates and to determine a confidence level for eachof said fused parameter estimates indicative of an accuracy of each ofsaid fused parameter estimates, said confidence calculator selecting oneof said fused parameter estimate as a final parameter estimates based onsaid confidence level.
 12. The automated data consolidation module ofclaim 11, wherein said filter circuit also receives a previous estimateof the final parameter estimate selected by said confidence calculator,said filter circuit producing said fused parameter estimates based onthe sensor measurements, said statistical model, and said previousestimate.
 13. The automated data consolidation module of claim 11,wherein said statistical model is a parameter variability statisticalmodel characterizing changes in the parameter over time and said filtercircuit produces said parameter estimates using said parametervariability statistical model.
 14. The automated data consolidationmodule of claim 13, further comprising a sensor measurement errorstatistical model characterizing susceptibility of the sensormeasurements to nominal error contamination, said filter circuitproducing said fused parameter estimates using said parametervariability statistical model and said sensor error statistical model.15. The automated data consolidation module of claim 11, wherein saidstatistical model is a sensor measurement error statistical modelcharacterizing susceptibility of the sensor measurements to nominalerror contamination, said filter circuit producing said parameterestimates using said sensor error statistical model.
 16. The automateddata consolidation module of claim 15, further comprising a parametervariability statistical model characterizing changes in the parameterover time, said filter circuit producing said parameter estimates usingsaid sensor error statistical model and said parameter variabilitystatistical model.
 17. The automated data consolidation module of claim11, wherein said confidence calculator alters said statistical modelfollowing the selection of said parameter estimate, whereby the systemis adaptive by using said final parameter estimate to alter saidstatistical model.
 18. The automated data consolidation module of claim11, wherein said filter circuit is a Kalman filter for each of saidpossible combinations, each of said Kalman filters using a previousparameter estimate and said statistical model to produce said fusedparameter estimates.
 19. The automated data consolidation module ofclaim 18, wherein said statistical model is a sensor measurement errorstatistical model having a Gaussian probability density functioncharacterizing susceptibility of the sensor measurements to nominalerror contamination, said Kalman filter using said Gaussian probabilitydensity function to produce said fused parameter estimates.
 20. Theautomated data consolidation module of claim 18, wherein saidstatistical model is a parameter variability model having a Gaussianprobability density function characterizing changes in the parameterover time, said Kalman filter using said Gaussian probability densityfunction to produce said fused parameter estimates.
 21. The automateddata consolidation module of claim 11, wherein the automated dataconsolidation module is configured to be used with physiological sensormeasurements that are susceptible to contamination by artifactual error,said contaminated physiological sensor measurements being consideredunacceptable, said confidence calculator analyzing said fused parameterestimates to determine a statistical probability of error of each ofsaid fused parameter estimates caused by said artifactual error.
 22. Theautomated data consolidation module of claim 21, wherein saidstatistical model is a sensor error statistical model characterizing thesusceptibility of the sensor measurements to nominal errorcontamination, said confidence calculator combining said sensor errorstatistical model and said statistical probability of contamination ofeach of said fused parameter estimates by said artifactual error todetermine said final parameter estimate.
 23. The automated dataconsolidation module of claim 21, wherein said confidence calculatorcalculates a minimum confidence value for one of said measurementcombinations for which all of the physiological sensor measurements areconsidered unacceptable, said confidence calculator further selectingcalculating a confidence value for remaining measurement combinationsusing a worst-case probability of contamination by said artifactualerror while also having a minimum probability of said confidence levelexceeding said minimum confidence level, said confidence calculatorselecting as said final parameter estimate the fused parameter estimatewith a maximum confidence value.
 24. A method for fusion ofphysiological sensor measurements in a subject having a plurality ofsensors coupled thereto, each of the sensors producing a sensormeasurement related to a physiological parameter, the method includingan automated data consolidation module for housing two or more pieces ofelectronic and electromechanical medical equipment and a method toreceive and analyze the data produced by the electronic andelectromechanical medical equipment using sensor fusion techniques, themethod for fusion of physiological sensor measurements comprising: ahousing configured to house the two or more pieces of electronic andelectromechanical medical equipment; a cowling that substantiallyconfines the two or more pieces of electronic and electromechanicalmedical equipment; processing circuitry in electrical communication withthe electronic and electromechanical medical equipment to receiveresponse event digital data produced by physiological sensors in theelectronic and electromechanical medical equipment; and softwareutilizing sensor fusion techniques that automatically compare the dataproduced by the two or more pieces of electronic and electromechanicalmedical equipment to determine if the data from one of the two or morepieces of electronic and electromechanical medical equipment iserroneous; wherein the sensor fusion techniques include: generating astatistical model affecting the fusion of the sensor measurements;producing a fused parameter estimate based on the sensor measurementsand said statistical model for each possible combination of the sensormeasurements from the plurality of sensors in which each of the sensormeasurements in said combinations are considered to be acceptable orunacceptable; determining a confidence level value for each of saidfused parameter estimates indicative of an accuracy of each of saidparameter estimates; and selecting a final parameter estimate based onsaid confidence level.
 25. The method of claim 24, wherein producingsaid fused parameter estimates includes basing said fused parameterestimates on the sensor measurements, said statistical model, and aprevious final parameter estimate.
 26. The method of claim 24, wherein:said statistical model is a parameter variability statistical modelcharacterizing changes in the parameter over time; and producing saidfused parameter estimates includes using said parameter variabilitystatistical model.
 27. The method of claim 26, further comprisinggenerating a sensor measurement error statistical model characterizingsusceptibility of the sensor measurements to nominal errorcontamination, wherein producing said fused parameter estimates includesusing said parameter variability statistical model and said sensor errorstatistical model.
 28. The method of claim 24, wherein: said statisticalmodel is a sensor measurement error statistical model characterizingsusceptibility of the sensor measurements to nominal errorcontamination; and producing said fused parameter estimates uses saidsensor measurement error statistical model.
 29. The method of claim 28,further comprising generating a parameter variability statistical modelcharacterizing changes in the parameter over time, wherein producingsaid fused parameter estimates uses said sensor measurement errorstatistical model and said parameter variability statistical model. 30.The method of claim 24, wherein producing said fused parameter estimatesincludes using a Kalman filter for each of said possible combinationsand using a previous final parameter estimate and said statistical modelin said Kalman filters to produce said fused parameter estimates.